Survey on social networking services
---
paper_title: User managed trust in social networking - Comparing Facebook, MySpace and Linkedin

paper_content:

Social Networks is the highest growing web-application in terms of users. Different surveys show that users are most concerned with their privacy in respect to web-based social networks. Anyhow, uses “compete” in the number of “friends” they can attach to their own profile. This means that the trust relations users are using to establish friends in the web applications becomes significantly different from the trust relations used in face-to-face meetings. This paper compares and discusses some of the existing self-management mechanisms in trust in three of the most used internet based social networking applications and suggests different aspects for dealing with trust from a user-centric design perspective.
---
paper_title: COMMUNITY PHOTO SHARING : MOTIVATIONAL AND STRUCTURAL ANTECEDENTS

paper_content:

In recent years there has seen a substantial growth of social computing. Content contribution from individual members is critical to the viability of social computing communities, and therefore it is important to understand what drives users to share content with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with users’ photo sharing in a social computing community. We draw on motivation theories as well as on network structural properties analysis, two critical concepts in predicting user behavior in communities. Our results indicate that users with higher commitment to the community and greater structural embeddedness tend to share more, whereas enjoyment shows no effect on the level of sharing. Counter to our expectations, the motivation of self-development was negatively related to photo sharing. Directions for further development of this research, as well as implications for theory and practice are discussed.
---
paper_title: Factor in the neighbors: Scalable and accurate collaborative filtering

paper_content:

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
---
paper_title: Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories

paper_content:

The biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to snare Knowledge with other members. This paper integrates the Social Cognitive Theory and the Social Capital Theory to construct a model for investigating the motivations behind people's knowledge sharing in virtual communities. The study holds that the facets of social capital -- social interaction ties, trust, norm of reciprocity, identification, shared vision and shared language -- will influence individuals' knowledge sharing in virtual communities. We also argue that outcome expectations -- community-related outcome expectations and personal outcome expectations -- can engender knowledge sharing in virtual communities. Data collected from 310 members of one professional virtual community provide support for the proposed model. The results help in identifying the motivation underlying individuals' knowledge sharing behavior in professional virtual communities. The implications for theory and practice and future research directions are discussed.
---
paper_title: Analysis of topological characteristics of huge online social networking services

paper_content:

Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.
---
paper_title: Understanding mobility based on GPS data

paper_content:

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.
---
paper_title: Understanding transportation modes based on GPS data for web applications

paper_content:

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
---
paper_title: Recommending friends and locations based on individual location history

paper_content:

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.
---
paper_title: A Survey on Context-aware systems

paper_content:

Context-aware systems offer entirely new opportunities for application developers and for end users by gathering context data and adapting systems behaviour accordingly. Especially in combination with mobile devices, these mechanisms are of high value and are used to increase usability tremendously. In this paper, we present common architecture principles of context-aware systems and derive a layered conceptual design framework to explain the different elements common to most context-aware architectures. Based on these design principles, we introduce various existing context-aware systems focusing on context-aware middleware and frameworks, which ease the development of context-aware applications. We discuss various approaches and analyse important aspects in context-aware computing on the basis of the presented systems.
---
paper_title: Privacy-enabling social networking over untrusted networks

paper_content:

Current social networks require users to place absolute faith in their operators, and the inability of operators to protect users from malicious agents has led to sensitive private information being made public. We propose an architecture for social networking that protects users' social information from both the operator and other network users. This architecture builds a social network out of smart clients and an untrusted central server in a way that removes the need for faith in network operators and gives users control of their privacy.
---
paper_title: A Survey of Context Modelling and Reasoning Techniques

paper_content:

Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of situations as abstractions of context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques.
---
paper_title: A Framework for Programming Robust Context-Aware Applications

paper_content:

In this paper, we present a forward recovery model for programming robust context-aware applications. The mechanisms devised as part of this model fall into two categories: asynchronous event handling and synchronous exception handling. These mechanisms enable designing recovery actions to handle different kinds of failure conditions arising in context-aware applications. These include service discovery failures, service binding failures, exceptions raised by a service, and context invalidations. This model is integrated in the high-level programming framework that we have designed for building context-aware collaborative (CSCW) applications. In this paper, we demonstrate the capabilities of this model for programming various kinds of recovery patterns in context-aware applications.
---
paper_title: Privacy-enabling social networking over untrusted networks

paper_content:

Current social networks require users to place absolute faith in their operators, and the inability of operators to protect users from malicious agents has led to sensitive private information being made public. We propose an architecture for social networking that protects users' social information from both the operator and other network users. This architecture builds a social network out of smart clients and an untrusted central server in a way that removes the need for faith in network operators and gives users control of their privacy.
---
paper_title: From the web of data to a world of action

paper_content:

This paper takes as its premise that the web is a place of action, not just information, and that the purpose of global data is to serve human needs. The paper presents several component technologies, which together work towards a vision where many small micro-applications can be threaded together using automated assistance to enable a unified and rich interaction. These technologies include data detector technology to enable any text to become a start point of semantic interaction; annotations for web-based services so that they can link data to potential actions; spreading activation over personal ontologies, to allow modelling of context; algorithms for automatically inferring 'typing' of web-form input data based on previous user inputs; and early work on inferring task structures from action traces. Some of these have already been integrated within an experimental web-based (extended) bookmarking tool, Snip!t, and a prototype desktop application On Time, and the paper discusses how the components could be more fully, yet more openly, linked in terms of both architecture and interaction. As well as contributing to the goal of an action and activity-focused web, the work also exposes a number of broader issues, theoretical, practical, social and economic, for the Semantic Web.
---
paper_title: Semantic Clustering Using Multiple Ontologies

paper_content:

Data mining tools able to semantically interpret textual or linguistic data are acquiring a growing importance. Moreover, the development of large ontologies for general and specific domains provides new tools to include background knowledge into data mining techniques such as clustering. In this paper we present an unsupervised clustering method that exploits the semantics of categorical data by means of ontologies, and it is also able to manage numerical data. Our method is able to use different ontologies in order to assess the meaning of the values during the clustering process, leading to a set of clusters with a clearer semantic interpretation in a particular domain. The influence of using one or several ontologies is analyzed by using real data collected from visitors to the Ebre Delta Natural Park, which is a protected natural reserve in Catalonia (Spain).
---
paper_title: A Framework for Programming Robust Context-Aware Applications

paper_content:

In this paper, we present a forward recovery model for programming robust context-aware applications. The mechanisms devised as part of this model fall into two categories: asynchronous event handling and synchronous exception handling. These mechanisms enable designing recovery actions to handle different kinds of failure conditions arising in context-aware applications. These include service discovery failures, service binding failures, exceptions raised by a service, and context invalidations. This model is integrated in the high-level programming framework that we have designed for building context-aware collaborative (CSCW) applications. In this paper, we demonstrate the capabilities of this model for programming various kinds of recovery patterns in context-aware applications.
---
paper_title: OSNAC: An Ontology-based Access Control Model for Social Networking Systems

paper_content:

As the information flowing around in social networking systems is mainly related or can be attributed to their users, controlling access to such information by individual users becomes a natural requirement. The intricate semantic relations among data objects, different users, and between data objects and users further add to the complexity of access control needs. In this paper, we propose an access control model based on Semantic Web technologies that takes into account the above mentioned complex relations. The proposed model enables expressing much more fine-grained access control policies on a social network knowledge base than the few existing models. We demonstrate the applicability of our approach by implementing a proof-of-concept prototype of the proposed access control framework.
---
paper_title: Semantic Clustering Using Multiple Ontologies

paper_content:

Data mining tools able to semantically interpret textual or linguistic data are acquiring a growing importance. Moreover, the development of large ontologies for general and specific domains provides new tools to include background knowledge into data mining techniques such as clustering. In this paper we present an unsupervised clustering method that exploits the semantics of categorical data by means of ontologies, and it is also able to manage numerical data. Our method is able to use different ontologies in order to assess the meaning of the values during the clustering process, leading to a set of clusters with a clearer semantic interpretation in a particular domain. The influence of using one or several ontologies is analyzed by using real data collected from visitors to the Ebre Delta Natural Park, which is a protected natural reserve in Catalonia (Spain).
---
paper_title: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web

paper_content:

The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world-changing. However, we argue that we are just at the beginning of this age of "social machines" and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.
---
paper_title: Towards a Model of Understanding Social Search

paper_content:

Search engine researchers typically depict search as the solitary activity of an individual searcher. In contrast, results from our critical-incident survey of 150 users on Amazon's Mechanical Turk service suggest that social interactions play an important role throughout the search process. Our main contribution is that we have integrated models from previous work in sensemaking and information seeking behavior to present a canonical social model of user activities before, during, and after search, suggesting where in the search process both explicitly and implicitly shared information may be valuable to individual searchers.
---
paper_title: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web

paper_content:

The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world-changing. However, we argue that we are just at the beginning of this age of "social machines" and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.
---
paper_title: Factor in the neighbors: Scalable and accurate collaborative filtering

paper_content:

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
---
paper_title: SearchTogether: an interface for collaborative web search

paper_content:

Studies of search habits reveal that people engage in many search tasks involving collaboration with others, such as travel planning, organizing social events, or working on a homework assignment. However, current Web search tools are designed for a single user, working alone. We introduce SearchTogether, a prototype that enables groups of remote users to synchronously or asynchronously collaborate when searching the Web. We describe an example usage scenario, and discuss the ways SearchTogether facilitates collaboration by supporting awareness, division of labor, and persistence. We then discuss the findings of our evaluation of SearchTogether, analyzing which aspects of its design enabled successful collaboration among study participants.
---
paper_title: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web

paper_content:

The advent of social computing on the Web has led to a new generation of Web applications that are powerful and world-changing. However, we argue that we are just at the beginning of this age of "social machines" and that their continued evolution and growth requires the cooperation of Web and AI researchers. In this paper, we show how the growing Semantic Web provides necessary support for these technologies, outline the challenges we see in bringing the technology to the next level, and propose some starting places for the research.
---
paper_title: Improving the effectiveness of collaborative recommendation with ontology-based user profiles

paper_content:

Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's specific context and to the changing shape of user interests over time. Our approach to building context-sensitive collaborative recommendation is a hybrid one that incorporates semantic knowledge in the form of a domain ontology. User profiles are defined relative to the ontology, giving rise to an ontological user profile. In this paper, we describe how ontological user profiles are learned, incrementally updated, and used for collaborative recommendation. Using book rating data, we demonstrate that this recommendation algorithm offers improved coverage, diversity, personalization, and cold-start performance while at the same time enhancing recommendation accuracy.
---
paper_title: Drawing Social Networks Using Area-Labeling Rectangular Cartograms

paper_content:

People are linked together by social networks, whose complexity depends upon a lot of factors. To our understanding, most visualization interfaces for social networks have not been designed for reflecting their factors so far. As a result, this paper tries to solve such a problem by rectangular cartogram, which is a kind of geographical visualization interface using rectangles to represent regions in a map. Besides the relative position of each rectangle can reflect actual geographical related positions, one of the main features of rectangular cartograms is to use the area size or the shape of each rectangle to reflect the information of its corresponding region, e.g., the population in that region. This paper proposes a layout approach for rectangular cartograms with area labeling for social networks, in which each region has a minimum-width constraint for accommodating a text label. To satisfy the practical use for social networks, we apply a genetic algorithm to finding the minimum-width area-labeling rectangular cartogram under some constraints. By doing so, we can visualize the labeling text on each rectangle and observe the information represented by its area size or shape at the same time. Furthermore, the proposed approach is applied to visualizing the distribution of the Facebook popularity of an enterprise in Taiwan. From the cartogram, the text label on each region can be read directly and the relation among regions as well as their popularity can be visualized at the same time, so that the enterprise can improve the regions with poor popularity by the help from the regions with high popularity.
---
paper_title: Crowds replace experts: Building better location-based services using mobile social network interactions

paper_content:

Location-based services are growing in popularity due to the ubiquity of smartphone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. The growing popularity of mobile social networks, such as Twitter, FourSquare and Facebook Places, presents an opportunity to build better location-based services by leveraging user interactions on these networks. In this paper, we present SocialTelescope, a location-based service that automatically compiles, indexes and ranks locations, based on user interactions with locations in mobile social networks. We implemented our system as a location-based search engine that uses geo-tweets by Twitter users to learn about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search), expert-based (Zagat) and user-review based (Yelp). Our results show that a crowd-sourced location-based service returns results that match those returned by current approaches in relevance, at a substantially lower cost.
---
paper_title: Knowledge infusion into content-based recommender systems

paper_content:

Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item). Common-sense and domain-specific knowledge may be useful to give some meaning to the content of items, thus helping to generate more informative features than "plain" attributes. The process of learning user profiles could also benefit from the infusion of exogenous knowledge or open source knowledge, with respect to the classical use of endogenous knowledge (extracted from the items themselves). The main contribution of this paper is a proposal for knowledge infusion into content-based recommender systems, which suggests a novel view of this type of systems, mostly oriented to content interpretation by way of the infused knowledge. The idea is to provide the system with the "linguistic" and "cultural" background knowledge that hopefully allows a more accurate content analysis than classic approaches based on words. A set of knowledge sources is modeled to create a memory of linguistic competencies and of more specific world "facts", that can be exploited to reason about content as well as to support the user profiling and recommendation processes. The modeled knowledge sources include a dictionary, Wikipedia, and content generated by users (i.e. tags provided on items), while the core of the reasoning component is a spreading activation algorithm.
---
paper_title: Web search personalization with ontological user profiles

paper_content:

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.
---
paper_title: Improving the effectiveness of collaborative recommendation with ontology-based user profiles

paper_content:

Collaborative recommendation is effective at representing a user's overall interests and tastes, and finding peer users that can provide good recommendations. However, it remains a challenge to make collaborative recommendation sensitive to a user's specific context and to the changing shape of user interests over time. Our approach to building context-sensitive collaborative recommendation is a hybrid one that incorporates semantic knowledge in the form of a domain ontology. User profiles are defined relative to the ontology, giving rise to an ontological user profile. In this paper, we describe how ontological user profiles are learned, incrementally updated, and used for collaborative recommendation. Using book rating data, we demonstrate that this recommendation algorithm offers improved coverage, diversity, personalization, and cold-start performance while at the same time enhancing recommendation accuracy.
---
paper_title: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

paper_content:

Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide context-aware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommendation improves upon non-contextual Matrix Factorization up to 30% in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware methods and show that Tensor Factorization consistently outperforms them both in semi-synthetic and real-world data - improvements range from 2.5% to more than 12% depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available.
---
paper_title: Enhancing Content-Based Recommendation with the Task Model of Classification

paper_content:

In this paper, we define reusable inference steps for content-based recommender systems based on semantically-enriched collections. We show an instantiation in the case of recommending artworks and concepts based on a museum domain ontology and a user profile consisting of rated artworks and rated concepts. The recommendation task is split into four inference steps: realization, classification by concepts, classification by instances, and retrieval. Our approach is evaluated on real user rating data. We compare the results with the standard content-based recommendation strategy in terms of accuracy and discuss the added values of providing serendipitous recommendations and supporting more complete explanations for recommended items.
---
paper_title: Emotion Attention to Friends on Social Networking Services

paper_content:

Social networking services are an important channel for people to keep in contact nowadays. Through the messages posted by friends, the user is able to know his/ her friends' recent activities, news and emotion status. In this research, we aimed at developing a system that can screen and analyze the messages posted by friends on social networking services to know which friends need instant attention/care. The posts on Facebook Wall of a friend are filtered first to obtain the messages by the friend, excluding the posts by games or other applications. The obtained messages are then classified into one of eight categories, including six emotion categories, special events, and others. The category information of the terms in the messages is then further analyzed for the proposed attention index. After the attention indices of selected friends are decided, they are ranked by the indices. The ones with higher attention indices need the user to show his/her care in a timely manner. We have developed a Facebook application that can display the desired information in a graphical interface.
---
paper_title: An Indoor Wireless System for Personalized Shopping Assistance

paper_content:

By integrating wireless, video, speech and real-time data access technologies, a unique shopping assistant service can be created that personalizes the attention provided to a custorner based on individual needs, without limiting his movement, or causing distractions from others in the shopping center. We have developed this idea into a service based on two products: a very high volume hand-held wireless communications device. the PSA (Personal Shopping Assistant), that the customer owns (or rnay be provided to a customer by the retailer), and a centralized server located in the shopping center to which the custorner communicates using the PSA. The centralized server maintains the customer database. the store database and provides audio/visual responses to inquiries fronr tens to hundreds of customers in real-time over a snrull areo wrteless network.
---
paper_title: Factor in the neighbors: Scalable and accurate collaborative filtering

paper_content:

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
---
paper_title: In justice we trust: Exploring knowledge-sharing continuance intentions in virtual communities of practice

paper_content:

Knowledge has become the key to success in the global knowledge economy, not only for organizations, but also in virtual communities of practice (VCoPs). The major challenge in sustaining a VCoP is acquiring knowledge spontaneously from members. This challenge leads to our research question: what encourages VCoP members to voluntarily and continuously help one another through continuous knowledge-sharing? In this study, we integrate three research streams-justice, trust, and organizational citizenship behaviors (OCB)-into one model in order to analyze the antecedents of knowledge-sharing continuance intentions in VCoPs. Our model theorizes that the four dimensions of justice (i.e., distributive, procedural, interpersonal, and informational justice) affect two different referents of trust (i.e., trust in members and trust in management). We further link these trust constructs to altruism (i.e., OCB directed to the individual) or conscientiousness (i.e., OCB directed to the organization), which in turn effect the knowledge-sharing continuance intentions in VCoPs. This hypothetical model is empirically validated using data collected from 142 members of an IT-oriented VCoP in Taiwan. Our integrated model has been helpful in VCoP research as it broadens our theoretical understanding of knowledge-sharing continuance intentions.
---
paper_title: Towards a Typology of Virtual Communities of Practice

paper_content:

The literature is packed with “one-size-fits-all” advice on how to develop intentionally formed virtual communities of practice (VCoPs). However, a closer look at the literature shows that VCoPs often have unique “personalities.” Based on an extensive review of the literature and a study of 18 VCoPs, we built a typology containing 21 structuring characteristics. We then used this typology and three of the studied VCoPs to show how different their basic natures are. Researchers and practitioners alike must not only recognize the diversity of VCoPs, but also identify challenges, strategies and practices that are contingent upon their specific characteristics.
---
paper_title: Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories

paper_content:

The biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to snare Knowledge with other members. This paper integrates the Social Cognitive Theory and the Social Capital Theory to construct a model for investigating the motivations behind people's knowledge sharing in virtual communities. The study holds that the facets of social capital -- social interaction ties, trust, norm of reciprocity, identification, shared vision and shared language -- will influence individuals' knowledge sharing in virtual communities. We also argue that outcome expectations -- community-related outcome expectations and personal outcome expectations -- can engender knowledge sharing in virtual communities. Data collected from 310 members of one professional virtual community provide support for the proposed model. The results help in identifying the motivation underlying individuals' knowledge sharing behavior in professional virtual communities. The implications for theory and practice and future research directions are discussed.
---
paper_title: Encouraging participation in virtual communities

paper_content:

Leaders of robust, sustainable virtual communities find ways tostrengthen their members' sense of social identity and motivate their participation in the community's activities.
---
paper_title: From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers' purchase intention

paper_content:

In China, major C2C websites are focusing on increasing their customer bases by converting members of their virtual communities (VCs) into C2C buyers and sellers. This phenomenon is called e-commerce based on social networks (ENS). The current research analyzes what factors affect trust building among VC members and how this trust influences the trust in the C2C website or vendor. We propose and empirically test a model of trust in VCs based on the trust formation mechanism. Using data collected from Taobao Virtual Community, we show that familiarity, perceived similarity, structural assurance, and trust propensity are important antecedents to trust in members in VCs. Analyses of the two kinds of trust show that trust in members' ability significantly affects three dimensions of trust in the vendor/website in terms of ability, integrity, and benevolence. In addition, trust in members' integrity and benevolence stimulates the purchase intention and trust in the vendor/website's ability positively affects the intention to get information and the purchase intention.
---
paper_title: Blog as a Tool to Develop e-Learning Experience in an International Distance Course

paper_content:

This paper describes the implementation of blog system in an international distance course between Japan and Taiwan. In the study, blog was used as a tool to encourage students' reflective learning and communication. Findings suggested that blog was effective for students to document their learning, share experience and knowledge, have direct interaction with peers especially internationally. Students preferred blog over the LMS where this course builds an course Web site and they proved to use blog in developing their e-learning experience. We proposed the use of blog could lead to a technologically enhanced support for instructional strategy that serves as an informative model for other Web-assisted international courses
---
paper_title: Educational applications of Web 2.0: Using blogs to support teaching and learning

paper_content:

This article presents the findings of a study conducted to determine whether blogs can be used as an effective educational tool. The research subjects were a class of postgraduate students at a university in Hong Kong, China over a period of a single semester. The author of the study focused on a primary question of whether blogs can contribute to an improved learning environment. He also examined the use of other Web 2.0 applications such as Wikies, social networking tools, and social book-marking tools in the classroom.
---
paper_title: Open Information Extraction Using Wikipedia

paper_content:

Information-extraction (IE) systems seek to distill semantic relations from natural-language text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform? ::: ::: This paper presents WOE, an open IE system which improves dramatically on TextRunner's precision and recall. The key to WOE's performance is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Like TextRunner, WOE's extractor eschews lexicalized features and handles an unbounded set of semantic relations. WOE can operate in two modes: when restricted to POS tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher.
---
paper_title: Microblogging Inside and Outside the Workplace

paper_content:

Microblogging has recently generated a lot of research interest. Yet very little is known about how corporate employees use microblogging tools. This study examined microblogging in the workplace by conducting a content analysis comparing posts from individuals who were using an internal proprietary tool and Twitter simultaneously. In both settings, posts that provided information or were directed to others were more common than posts on status. Within these categories, it was more frequent to provide information externally than internally but more common to ask questions either through broadcast or directed posts internally than externally. Qualitative interviews explored users’ motivations regarding microblogging behavior. The paper concludes with a discussion of the implications of microblogging for business use.
---
paper_title: Microblogging during two natural hazards events: what twitter may contribute to situational awareness

paper_content:

We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.
---
paper_title: Evolution of Social Networks Based on Tagging Practices

paper_content:

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using "tags” or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. In this paper, we introduce an object-centered social network based on tagging practices across different sources, and then we show how this network can be built and emerged over time.
---
paper_title: User Modelling for News Web Sites with Word Sense Based Techniques

paper_content:

SiteIF is a personal agent for a bilingual news web site that learns user's interests from the requested pages. In this paper we propose to use a word sense based document representation as a starting point to build a model of the user's interests. Documents passed over are processed and relevant senses (disambiguated over WordNet) are extracted and then combined to form a semantic network. A filtering procedure dynamically predicts new documents on the basis of the semantic network. ::: ::: There are two main advantages of a sense-based approach: first, the model predictions, being based on senses rather than words, are more accurate; second, the model is language independent, allowing navigation in multilingual sites. We report the results of a comparative experiment that has been carried out to give a quantitative estimation of these improvements.
---
paper_title: Social news, citizen journalism and democracy

paper_content:

This article aims to contribute to a critical research agenda for investigating the democratic implications of citizen journalism and social news. The article calls for a broad conception of ‘citizen journalism’ which is (1) not an exclusively online phenomenon, (2) not confined to explicitly ‘alternative’ news sources, and (3) includes ‘metajournalism’ as well as the practices of journalism itself. A case is made for seeing democratic implications not simply in the horizontal or ‘peer-to-peer’ public sphere of citizen journalism networks, but also in the possibility of a more ‘reflexive’ culture of news consumption through citizen participation. The article calls for a research agenda that investigates new forms of gatekeeping and agenda-setting power within social news and citizen journalism networks and, drawing on the example of three sites, highlights the importance of both formal and informal status differentials and of the software ‘code’ structuring these new modes of news production.
---
paper_title: Document recommendation in social tagging services

paper_content:

Social tagging services allow users to annotate various online resources with freely chosen keywords (tags). They not only facilitate the users in finding and organizing online resources, but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems. Traditional studies on recommender systems focused on user rating data, while recently social tagging data is becoming more and more prevalent. How to perform resource recommendation based on tagging data is an emerging research topic. In this paper we consider the problem of document (e.g. Web pages, research papers) recommendation using purely tagging data. That is, we only have data containing users, tags, documents and the relationships among them. We propose a novel graph-based representation learning algorithm for this purpose. The users, tags and documents are represented in the same semantic space in which two related objects are close to each other. For a given user, we recommend those documents that are sufficiently close to him/her. Experimental results on two data sets crawled from Del.icio.us and CiteULike show that our algorithm can generate promising recommendations and outperforms traditional recommendation algorithms.
---
paper_title: Analysis of participation in an online photo-sharing community: A multidimensional perspective

paper_content:

In recent years we have witnessed a significant growth of social-computing communities—online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo-sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed. © 2010 Wiley Periodicals, Inc.
---
paper_title: Social Browsing on Flickr

paper_content:

The new social media sites—blogs, wikis, del.icio.us and Flickr, among others—underscore the transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing information. The photo-sharing site Flickr, for example, allows users to upload photographs, view photos created by others, comment on those photos, etc. As is common to other social media sites, Flickr allows users to designate others as "contacts" and to track their activ- ities in real time. The contacts (or friends) lists form the social network backbone of social media sites. These social networks facilitate new ways of interacting with information, e.g., through what we call social browsing. The contacts inter- face on Flickr enables users to see latest images submitted by their friends. Through an extensive analysis of Flickr data, we show that social browsing through the contacts' photo streams is one of the primary methods by which users find new images on Flickr. This finding has implications for creat- ing personalized recommendation systems based on the user's declared contacts lists.
---
paper_title: COMMUNITY PHOTO SHARING : MOTIVATIONAL AND STRUCTURAL ANTECEDENTS

paper_content:

In recent years there has seen a substantial growth of social computing. Content contribution from individual members is critical to the viability of social computing communities, and therefore it is important to understand what drives users to share content with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with users’ photo sharing in a social computing community. We draw on motivation theories as well as on network structural properties analysis, two critical concepts in predicting user behavior in communities. Our results indicate that users with higher commitment to the community and greater structural embeddedness tend to share more, whereas enjoyment shows no effect on the level of sharing. Counter to our expectations, the motivation of self-development was negatively related to photo sharing. Directions for further development of this research, as well as implications for theory and practice are discussed.
---
paper_title: Application in Social Network English Learning Based on Virtual Cloud Technology Combined with Essential Articles Classification

paper_content:

When searching for articles relating to English grammar, general search engines only compare keywords but do not analyze whether the articles are related to learning. This study proposed an English grammar learning articles classification system that could analyze the collected articles. The proposed system used TF-IDF to summarize the important information of the articles and the neural text categorizer neural network architecture for training the classification and summarization of articles relating to learning English. As the computational cost of such work was high, a private cloud structure was built using multiple virtual machines to enhance the effectiveness in all respects and help learners find accurate English grammar learning articles. The concept of social networking was combined with the English platform adaptive test mechanism developed by this study, which allowed the learners to search for appropriate English learning articles. The learners were asked to provide their opinions and feedback on the found articles, which were ultimately sent back to the proposed cloud text classification system to determine the most suitable articles for improving the English level of the learners.
---
paper_title: Analysis of participation in an online photo-sharing community: A multidimensional perspective

paper_content:

In recent years we have witnessed a significant growth of social-computing communities—online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo-sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed. © 2010 Wiley Periodicals, Inc.
---
paper_title: Virtual worlds - past, present, and future: New directions in social computing

paper_content:

Virtual worlds, where thousands of people can interact simultaneously within the same three-dimensional environment, represent a frontier in social computing with critical implications for business, education, social sciences, and our society at large. In this paper, we first trace the history of virtual worlds back to its antecedents in electronic gaming and on-line social networking. We then provide an overview of extant virtual worlds, including education-focused, theme-based, community-specific, children-focused, and self-determined worlds - and we analyze the relationship among these worlds according to an initial taxonomy for the area. Recognizing the apparent leadership of Second Life among today's self-determined virtual worlds, we present a detailed case study of this environment, including surveys of 138 residents regarding how they perceive and utilize the environment. Lastly, we provide a literature review of existing virtual world research, with a focus on business research, and a condensed summary of research issues in education, social sciences, and humanities.
---
paper_title: How the Human Brain Goes Virtual: Distinct Cortical Regions of the Person-Processing Network Are Involved in Self-Identification with Virtual Agents

paper_content:

Millions of people worldwide engage in online role-playing with their avatar, a virtual agent that represents the self. Previous behavioral studies have indicated that many gamers identify more strongly with their avatar than with their biological self. Through their avatar, gamers develop social networks and learn new social-cognitive skills. The cognitive neurosciences have yet to identify the neural processes that underlie self-identification with these virtual agents. We applied functional neuroimaging to 22 long-term online gamers and 21 nongaming controls, while they rated personality traits of self, avatar, and familiar others. Strikingly, neuroimaging data revealed greater avatar-referential cortical activity in the left inferior parietal lobe, ar egion associated with self-identif ication from at hird-person perspective. The magnitude of this brai na ctivity correlated positively with the propensity to incorporat ee xternal body enhancements into one’s bodily identity. Avatar-referencing furthermore recruited greater activity in the rostral anterior cingulate gyrus, suggesting relatively greater emotional self-involvement with one’s avatar. Post-scanning behavioral data revealed superior recognition memory for avatar relative to others. Interestingly, memory for avatar positively covaried with play duration. These findings significantly advance our knowledge about the brain’s plasticity to self-identify with virtual agents and the human cognitive-affective potential t ol ive and learn in virtual worlds.
---
paper_title: Avatars, People, and Virtual Worlds: Foundations for Research in Metaverses *

paper_content:

Metaverses are immersive three-dimensional virtual worlds in which people interact as avatars with each other and with software agents, using the metaphor of the real world but without its physical limitations. The ubiquitous availability of high speed Internet access has spurred enormous interest in virtual worlds like Second Life and World of Warcraft, both in terms of user gaming and as a new technological platform for global virtual collaboration. These environments have potential for richer, more engaging collaboration, but their capabilities have yet to be examined in depth. Of particular interest in this paper is the use of metaverses for virtual team collaboration. We develop a conceptual model for research in metaverses that is based on five key constructs: (1) the metaverse itself, (2) people/avatars, (3) metaverse technology capabilities, (4) behaviors, and (5) outcomes. We present an indepth characterization of metaverse technology capabilities from a socio-technical view that recognizes the potential for variation in emergent interaction and in outcomes. Example propositions and a discussion of key issues and challenges show how the model can be used to further research and practice in virtual teams in the context of these new environments.
---
paper_title: Maximum Entropy-Based Named Entity Recognition Method for Multiple Social Networking Services

paper_content:

Given a certain question, named entity recognition (NER) methods are regarded as an efficient strategy to extract correct answers. The goal of this work is to extend such conventional NER methods for analyzing a set of microtexts of which lengths are relatively short. These microtexts are streaming through several different social networking services, e.g., Twitter and FaceBook. To do so, we propose three heuristics for determining contextual associations between the microtexts, and discovering contextual clusters of microtexts, which can be expected to improve the performance of conventional NER tasks. Experimental results show the feasibility of the proposed mechanisms which extend the maximum entropy-based NER tasks for extracting relevant information in online social network applications.
---
paper_title: Understanding mobility based on GPS data

paper_content:

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.
---
paper_title: Understanding transportation modes based on GPS data for web applications

paper_content:

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
---
paper_title: Where We Were: Communities for Sharing Space-Time Trails

paper_content:

We consider trails to be a document type of growing importance, authored in abundance as locative technologies become embedded in mobile devices carried by billions of humans. As these trail documents become annotated by communities of users, the resulting data sets can provide support for a host of services. In this paper we describe our sociotechnical exploration of the devices, scenarios, and end-user interactions that will come into play as these tools become widespread. We couch this work in a discussion of the sociological impact of a shift from hyperlinks to "hyperties" -- links that bridge the gap between computational media and physical world interactions. We describe a prototype hardware device for location and other sensor data capture. This device links to a complementary website for querying, sharing, and distributing the resulting route datasets. The web application allows users to find related community members via shared attributes of their contributed or annotated routes. These attributes may be generated in part by route analysis performed by systems for activity identification and classification.
---
paper_title: Recommending friends and locations based on individual location history

paper_content:

The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.
---
paper_title: User Modelling for News Web Sites with Word Sense Based Techniques

paper_content:

SiteIF is a personal agent for a bilingual news web site that learns user's interests from the requested pages. In this paper we propose to use a word sense based document representation as a starting point to build a model of the user's interests. Documents passed over are processed and relevant senses (disambiguated over WordNet) are extracted and then combined to form a semantic network. A filtering procedure dynamically predicts new documents on the basis of the semantic network. ::: ::: There are two main advantages of a sense-based approach: first, the model predictions, being based on senses rather than words, are more accurate; second, the model is language independent, allowing navigation in multilingual sites. We report the results of a comparative experiment that has been carried out to give a quantitative estimation of these improvements.
---
paper_title: E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity

paper_content:

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish nonservice-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.
---
paper_title: Cluestr: mobile social networking for enhanced group communication

paper_content:

Recent technological advances foster the spreading of social software in the mobile domain. Hence, future usage patterns of mobile devices will involve more group interaction. While collaboration using mobile devices is an active area of research, only limited attention has been paid to the efficient initiation of group communication from mobile terminals. In this paper we present a community-aware mechanism that allows to efficiently select contacts in order to address them as a group. We have integrated the proposed method into a proof-of-concept application, and present preliminary experiments that demonstrate the accuracy of the approach and show significant time savings in the group initialization process.
---
paper_title: SPac: a distributed, peer-to-peer, secure and privacy-aware social space

paper_content:

To support privacy-aware management of data in social spaces, the user personal data needs to be stored at each user device, and shared only with a trusted subset of other users. To date, social spaces only have fairly limited access control capabilities, that do not protect the possibly sensitive data of the users. In this demonstration, we showcase our SPAC system, a distributed, peer-to-peer, secure and privacy-aware social space system. SPAC is equipped with: (i) an SQL-based declarative distributed query language to specify which data to share and whom to share with. Such a language guarantees the fine-grained access to the data, (ii) a fully-decentralized authorization that relies on classic cryptographic protocols to provide robust and resilient key-based encryption for access control enforcement, and (iii) an update-friendly access control mechanism, that also addresses the updates on both the network and the access control policies.
---
paper_title: Privacy-enabling social networking over untrusted networks

paper_content:

Current social networks require users to place absolute faith in their operators, and the inability of operators to protect users from malicious agents has led to sensitive private information being made public. We propose an architecture for social networking that protects users' social information from both the operator and other network users. This architecture builds a social network out of smart clients and an untrusted central server in a way that removes the need for faith in network operators and gives users control of their privacy.
---
paper_title: NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing

paper_content:

The recent three years have witnessed an explosion of networked video sharing, represented by YouTube, as a new killer Internet application. Their sustainable development however is severely hindered by the intrinsic limit of their client/server architecture. A shift to the peer-to-peer paradigm has been widely suggested with success already shown in live video streaming and movie-on-demand. Unfortunately, our latest measurement demonstrates that short video clips exhibit drastically different statistics, which would simply render these existing solutions suboptimal, if not entirely inapplicable. Our long-term measurement over five million YouTube videos, on the other hand, reveal interesting social networks with strong clustering among the videos, thus opening new opportunities to explore. In this paper, we present NetTube, a novel peer-to- peer assisted delivering framework that explores the clustering in social networks for short video sharing. We address a series of key design issues to realize the system, including a bi-layer overlay, an efficient indexing scheme and a pre-fetching strategy leveraging social networks. We evaluate NetTube through simulations and prototype experiments, which show that it greatly reduces the server workload, improves the playback quality and scales well.
---
paper_title: Stir: Spontaneous social peer-to-peer streaming

paper_content:

Dealing with a high churn rate is very challenging in live peer-to-peer streaming. State-of-the-art studies try to mitigate the problem by exploiting peer dynamic models, analyzing traces from real world systems, or using enhanced coding techniques, e.g., network coding. Applications of social networking in peer-to-peer systems, especially on file sharing, have recently received research attention. In such systems, the establishment of connections among peers is based on social relationships among users, which are, however, not formed in the context of a peer-to-peer session but, e.g., imported from other social networks. Since friends in such a separate social network do not always have similar interests, they may not necessarily join or stay long in the same peer-to-peer session. We believe that a tight integration between the high level social network of users and the low level overlay of peers would bring significant benefits in dealing with high churn rates and providing personalized streaming services. This paper presents Stir, the first attempt towards an integrated social peer-to-peer streaming system. The key feature of Stir is that social relationships among users are spontaneously formed in a streaming session, and can be exploited directly by the underlying streaming protocol. Stir users, who join the same session, can make friends by means of spontaneous communication, e.g., instant messaging. Such social network formation provides a reliable indication to deal with high churn rate. Our simulations with real social data and peer dynamic traces have demonstrated the benefits of Stir and shed light on building such a system in practice.
---
paper_title: Understanding transportation modes based on GPS data for web applications

paper_content:

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
---

Title: Survey on Social Networking Services

Section 1: Introduction
Description 1: Introduce the growth of social computing and the need for novel theories and methodologies, highlighting the different types of SNSs and the importance of context-awareness.

Section 2: Context-based SNSs
Description 2: Discuss the integration of contextual information in social computing and characterise different types of context-based SNSs.

Section 3: Social Semantics as SNSs for Knowledge Management
Description 3: Explore the use of social semantic Web and ontologies for knowledge management in context-aware SNSs.

Section 4: Social Search as SNSs for Knowledge Searching
Description 4: Examine the role of collaboration in social searches and various social search models.

Section 5: Context-aware Recommendation Systems as SNSs
Description 5: Describe the evolution of web-based recommendation systems, including content-driven and collaborative-filtering-based systems, and the introduction of context-aware recommendation systems (CARSs).

Section 6: Content-based SNSs
Description 6: Analyze various types of content-based SNSs, including community-based SNSs, blogs, micro-blogging, social news, and tagging.

Section 7: Media-based SNSs
Description 7: Review different media-based SNSs that enable social connection through multimedia formats like video and audio, including photo and video sharing and social virtual worlds.

Section 8: Geo-location-based Social Networking
Description 8: Discuss location-based SNSs and the research issues related to their implementation, such as privacy and up-to-date information.

Section 9: Privacy in SNSs
Description 9: Address the privacy concerns related to the use of SNSs, and discuss various privacy-enabling architectures and the concept of distributed social networks.

Section 10: Future Work
Description 10: Highlight areas needing significant research, such as social search, interoperability, scalability, and privacy, emphasizing the promise of machine learning and text mining techniques.

Section 11: Conclusions
Description 11: Summarize the evolution and significance of SNSs, discussing the importance of context-aware technologies and future research opportunities.