A Survey of User Modelling in Social Media Websites
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paper_title: Social media? Get serious! Understanding the functional building blocks of social media

paper_content:

Traditionally, consumers used the Internet to simply expend content: they read it, they watched it, and they used it to buy products and services. Increasingly, however, consumers are utilizing platforms--such as content sharing sites, blogs, social networking, and wikis--to create, modify, share, and discuss Internet content. This represents the social media phenomenon, which can now significantly impact a firm's reputation, sales, and even survival. Yet, many executives eschew or ignore this form of media because they don't understand what it is, the various forms it can take, and how to engage with it and learn. In response, we present a framework that defines social media by using seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. As different social media activities are defined by the extent to which they focus on some or all of these blocks, we explain the implications that each block can have for how firms should engage with social media. To conclude, we present a number of recommendations regarding how firms should develop strategies for monitoring, understanding, and responding to different social media activities.
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paper_title: Users of the world, unite! The challenges and opportunities of Social Media

paper_content:

The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ''Social Media'' exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
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paper_title: The state-of-the-art in personalized recommender systems for social networking

paper_content:

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users' personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.
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paper_title: Dynamic recommender system : using cluster-based biases to improve the accuracy of the predictions

paper_content:

It is today accepted that matrix factorization models allow a high quality of rating prediction in recommender systems. However, a major drawback of matrix factorization is its static nature that results in a progressive declining of the accuracy of the predictions after each factorization. This is due to the fact that the new obtained ratings are not taken into account until a new factorization is computed, which can not be done very often because of the high cost of matrix factorization. ::: In this paper, aiming at improving the accuracy of recommender systems, we propose a cluster-based matrix factorization technique that enables online integration of new ratings. Thus, we significantly enhance the obtained predictions between two matrix factorizations. We use finer-grained user biases by clustering similar items into groups, and allocating in these groups a bias to each user. The experiments we did on large datasets demonstrated the efficiency of our approach.
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paper_title: Temporal recommendation on graphs via long- and short-term preference fusion

paper_content:

Accurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can often be determined by individual's long-term and short-term preferences. How to represent users' long-term and short-term preferences? How to leverage them for temporal recommendation? To address these challenges, we propose Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time. Based on the STG model framework, we propose a novel recommendation algorithm Injected Preference Fusion (IPF) and extend the personalized Random Walk for temporal recommendation. Finally, we evaluate the effectiveness of our method using two real datasets on citations and social bookmarking, in which our proposed method IPF gives 15%-34% improvement over the previous state-of-the-art.
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paper_title: Can you hear me now? Learning from customer stories

paper_content:

A large business generally knows its customers through quantitative analyses and summary reports, which are staples of market research and corporate reporting. Too often, the voices of individual customers are muffled by a torrent of numbers, and the stories they would tell are garbled. Few managers and even fewer executives hear customers speak, in their own words and ways, about the company's products and services. Yet, as we discuss, these stories may suggest enhanced customer service, better products, and organizational innovation; indeed, a number of companies have shown that the value of attentiveness to customer stories can be great. Herein, we describe a controlled, exploratory approach a company might take to develop its own engagement with customer storytelling—one that encompasses not only its interactions with customers along the front line, but also conversations ongoing in social media. Our proposal demands sustained commitment from senior management. We counsel senior executives to lead by example, to listen to customer stories, to learn from them, and to share them with others in the executive suite. By truly caring about storytelling, business leaders can better serve their customers and their companies.
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paper_title: Scalable distributed inference of dynamic user interests for behavioral targeting

paper_content:

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
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paper_title: We're all connected: The power of the social media ecosystem

paper_content:

Consumers are adopting increasingly active roles in co-creating marketing content with companies and their respective brands. In turn, companies and organizations are looking to online social marketing programs and campaigns in an effort to reach consumers where they 'live' online. However, the challenge facing many companies is that although they recognize the need to be active in social media, they do not truly understand how to do it effectively, what performance indicators they should be measuring, and how they should measure them. Further, as companies develop social media strategies, platforms such as YouTube, Facebook, and Twitter are too often treated as stand-alone elements rather than part of an integrated system. This article offers a systematic way of understanding and conceptualizing online social media, as an ecosystem of related elements involving both digital and traditional media. We highlight a best-practice case study of an organization's successful efforts to leverage social media in reaching an important audience of young consumers. Then, we conclude with several insights and lessons related to the strategic integration of social media into a firm's marketing communications strategy.
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paper_title: Let them talk! Managing primary and extended online brand communities for success

paper_content:

It is clear that customer-to-customer contact through informal social networking and more formal company-sponsored platforms, such as discussion forums, is an increasingly integral element in building brand communities. There are many benefits to this interaction, such as nurturing brand champions and ‘super users,’ and reduced service costs through customer-to-customer solutions for product problems. However, there are also hazards inherent in these largely unregulated communities, such as the potential damage of widely spread negative information, which may be based on fact or on malicious intent. Herein, we summarize the results of several years of research examining these communities in an attempt to understand why they succeed, what benefits can be extracted from them, and—in particular—how negative information emerging in these environments can be strategically managed. Based on a series of quantitative and qualitative studies, we identified several key drivers of online brand community success (i.e., intervention, conversion, value creation, and harvesting) and the different combinations of community players who must collaborate to achieve such success. Delving more deeply into the issue of negative information management, we find that the topic being discussed (i.e., core versus augmented product) and the validity of the claim greatly influence a firm's appropriate strategic response. Throughout this article, we offer managerial guidance on the most effective ways to develop primary brand communities that encourage loyalty, purchases, and positive word of mouth.
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paper_title: Intelligent Techniques for Web Personalization

paper_content:

In this chapter we provide a comprehensive overview of the topic of Intelligent Techniques for Web Personalization. Web Personalization is viewed as an application of data mining and machine learning techniques to build models of user behaviour that can be applied to the task of predicting user needs and adapting future interactions with the ultimate goal of improved user satisfaction. This chapter survey's the state-of-the-art in Web personalization. We start by providing a description of the personalization process and a classification of the current approaches to Web personalization. We discuss the various sources of data available to personalization systems, the modelling approaches employed and the current approaches to evaluating these systems. A number of challenges faced by researchers developing these systems are described as are solutions to these challenges proposed in literature. The chapter concludes with a discussion on the open challenges that must be addressed by the research community if this technology is to make a positive impact on user satisfaction with the Web.
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paper_title: Circle-based recommendation in online social networks

paper_content:

Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems (RS). As to better serve users' activities across different domains, many online social networks now support a new feature of "Friends Circles", which refines the domain-oblivious "Friends" concept. RS should also benefit from domain-specific "Trust Circles". Intuitively, a user may trust different subsets of friends regarding different domains. Unfortunately, in most existing multi-category rating datasets, a user's social connections from all categories are mixed together. This paper presents an effort to develop circle-based RS. We focus on inferring category-specific social trust circles from available rating data combined with social network data. We outline several variants of weighting friends within circles based on their inferred expertise levels. Through experiments on publicly available data, we demonstrate that the proposed circle-based recommendation models can better utilize user's social trust information, resulting in increased recommendation accuracy.
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paper_title: Personalisation in web computing and informatics: Theories, techniques, applications, and future research

paper_content:

Recently, personalised search engines and recommendation systems have been widely adopted by users who require assistance in searching, classifying, and filtering information. This paper presents an overview of the field of personalisation systems and describes current state-of-the-art methods and techniques. It reviews approaches for (1) user profiling, including behaviour, preference, and intention modelling; (2) content modelling, comprising content representation, analysis, and classification; and (3) filtering methods for recommendation, classified into four main categories: rule-based, content-based, collaborative, and hybrid filtering. The paper also discusses personalisation systems in different domains, and various techniques and their limitations. Finally, it identifies several issues and possible directions for further research that can improve recommendation capabilities and enhance personalised systems.
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paper_title: TUMS: Twitter-based User Modeling Service

paper_content:

Twitter is today's most popular micro-blogging service on the Social Web. As people discuss various fresh topics, Twitter messages (tweets) can tell much about the current interests and concerns of a user. In this paper, we introduce TUMS, a Twitter-based User Modeling Service, that infers semantic user profiles from the messages people post on Twitter. It features topic detection and entity extraction for tweets and allows for further enrichment by linking tweets to news articles that describe the context of the tweets. TUMS is made publicly available as a Web application. It allows end-users to overview Twitter-based profiles in a structured way and allows them to see in which topics or entities a user was interested at a specific point in time. Furthermore, it provides Twitter-based user profiles in RDF format and allows applications to incorporate these profiles in order to adapt their functionality to the current interests of a user. TUMS is available via: http://wis.ewi.tudelft.nl/tums/
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paper_title: Recommender systems with social regularization

paper_content:

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
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paper_title: Adaptive social similarities for recommender systems

paper_content:

Collaborative filtering (CF) is an effective recommendation technique, which selects items for an individual user based on similar users' preferences. However, CF may not fully reflect the procedure how people choose an item in real life, for users are more likely to ask friends for opinions instead of asking similar strangers. Recently, some recommendation methods based on social network have been raised. These approaches incorporate social network into the CF algorithms and users' preferences can be influenced by the favors of their friends. These social approaches require the knowledge of similarities among friends. There are two popular similarity functions: Vector Space Similarity (VSS) and Pearson Correlation Coefficient (PCC). However, both friends similarity functions are based on the item-sets they rated in common. In most cases, these functions are impractical, i.e. if two friends do not share the same items in common, the similarity between them will be zeros. To solve this problem, we propose an Adaptive Social Similarity (ASS) function based on the matrix factorization technique. We conduct our experiment on a large dataset: Epinions, which is a widely-used dataset with social information. The experiment results illustrate that our approach outperforms the baseline models and achieves a better performance than social-based method in [4].
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paper_title: A user profile modelling using social annotations: a survey

paper_content:

As social networks are growing in terms of the number of users, resources and interactions; the user may be lost or unable to find useful information. Social elements could avoid this disorientation like the social annotations (tags) which become more and more popular and contribute to avoid the disorientation of the user. Representing a user based on these social annotations has showed their utility in reflecting an accurate user profile which could be used for a recommendation purpose. In this paper, we give a state of the art of characteristics of social user and techniques which model and update a tag-based profile. We show how to treat social annotations and the utility of modelling tag-based profiles for recommendation purposes.
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paper_title: User profiles for personalized information access

paper_content:

The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented.
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paper_title: The state-of-the-art in personalized recommender systems for social networking

paper_content:

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users' personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.
---
paper_title: Collaborative user modeling for enhanced content filtering in recommender systems

paper_content:

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.
---
paper_title: Recommender systems with social regularization

paper_content:

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
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paper_title: Context aware recommendations for user-generated content on a social network site

paper_content:

The enormous offer of video content on the internet and its continuous growth make the selection process increasingly difficult for end-users. This overabundance of audio-visual material can be handled by a recommendation system that observes user preferences and assists people with finding interesting content. However, present-day recommendation systems focus on the metadata or the previous consumption behaviour to select the content but do not consider contextual information or social network relations. Therefore, we developed a tag cloud based recommendation system for user-generated content which exploits these social network relations. Recommendations based on the user's profile are supplemented with social recommendations: content suggestions from people on the user's contact list. Moreover, since we believe that the consumption context (location, time, etc.) has a significant influence on the content selection process, the system records all the available context information. Our next task is to analyze the obtained dataset and to determine the influence of the individual context features on the consumption behaviour. The system recommendations and social recommendations will be compared on the basis of effectiveness, novelty and user appreciation. Finally, we intend to incorporate the results of this analysis in our personalization algorithm in order to improve the recommendation results.
---
paper_title: A user profile modelling using social annotations: a survey

paper_content:

As social networks are growing in terms of the number of users, resources and interactions; the user may be lost or unable to find useful information. Social elements could avoid this disorientation like the social annotations (tags) which become more and more popular and contribute to avoid the disorientation of the user. Representing a user based on these social annotations has showed their utility in reflecting an accurate user profile which could be used for a recommendation purpose. In this paper, we give a state of the art of characteristics of social user and techniques which model and update a tag-based profile. We show how to treat social annotations and the utility of modelling tag-based profiles for recommendation purposes.
---
paper_title: Recommending twitter users to follow using content and collaborative filtering approaches

paper_content:

Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.
---
paper_title: Collaborative user modeling for enhanced content filtering in recommender systems

paper_content:

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.
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paper_title: An algorithm for automated rating of reviewers

paper_content:

The current system for scholarly information dissemination may be amen able to significant improvement. In particular, going from the current system of journal publication to one of self-distributed documents offers significant cost and timeliness advantages. A major concern with such alternatives is how to provide the value currently afforded by the peer review system. Here we propose a mechanism that could plausibly supply such value. In the peer review system, papers are judged meritorious if good reviewers give them good reviews. In its place, we propose a collaborative filtering algorithm which automatically rates reviewers, and incorporates the quality of the reviewer into the metric of merit for the paper. Such a system seems to provide all the benefits of the current peer review system, while at the same time being much more flexible. We have implemented a number of parameterized variations of this algorithm, and tested them on data available from a quite different application. Our initial experiments suggest that the algorithm is in fact ranking reviewers reasonably.
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paper_title: Google news personalization: scalable online collaborative filtering

paper_content:

Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.
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paper_title: Circle-based recommendation in online social networks

paper_content:

Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems (RS). As to better serve users' activities across different domains, many online social networks now support a new feature of "Friends Circles", which refines the domain-oblivious "Friends" concept. RS should also benefit from domain-specific "Trust Circles". Intuitively, a user may trust different subsets of friends regarding different domains. Unfortunately, in most existing multi-category rating datasets, a user's social connections from all categories are mixed together. This paper presents an effort to develop circle-based RS. We focus on inferring category-specific social trust circles from available rating data combined with social network data. We outline several variants of weighting friends within circles based on their inferred expertise levels. Through experiments on publicly available data, we demonstrate that the proposed circle-based recommendation models can better utilize user's social trust information, resulting in increased recommendation accuracy.
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paper_title: Developing Trust Networks Based on User Tagging Information for Recommendation Making

paper_content:

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user's interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user's personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.
---
paper_title: Recommender systems with social regularization

paper_content:

Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
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paper_title: Towards social-based user modeling and personalization

paper_content:

As the Web grows and the amount of available informa- tion increases, new problems such as information over- load or lost-in-hyperspace problem emerge. The solution is to shift from \one-size-ts-all " approach and to pro- vide personalized surng experience, which would take into account dierences among users. These dierences are captured in a user model, a structure holding all rel- evant user-related information. We present contributions in both data collection and processing stages of user mod- eling process, focusing on open corpus web-based sys- tems, where the content can be dynamically added or changed and we have no content available in the design stage of the web-based system. We introduce two meth- ods falling within the scope of data collection stage, a method for comprehensive logging of user activity on the Web with preserved semantics, which combines client side and server side logging into a stream of user events with clearly dened meaning, and a method for capturing logs of \wild" Web surng based on a specialized proxy sever. The second group of methods, devoted to actual user model creation within an open corpus environment con- sists of a method for user model inference based on rules expressing navigational patterns, a method for term-based open corpus user modeling, which can be applied to cap- ture user's interest across the third-party web-sites and web-based systems. We proposed also a method for nd- ing relations between terms based on folksonomies, which supports our term-based user modeling approach. The proposed methods were evaluated by means of software tools that were incorporated in research projects aimed
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paper_title: A vector space model for automatic indexing

paper_content:

In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.
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paper_title: Short and tweet: experiments on recommending content from information streams

paper_content:

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.
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paper_title: Recommending twitter users to follow using content and collaborative filtering approaches

paper_content:

Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.
---
paper_title: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling

paper_content:

Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes the integration of linguistic knowledge in the process of learning semantic user profiles that capture concepts concerning user interests. The proposed strategy consists of two steps. The first one is based on a word sense disambiguation technique that exploits the lexical database WordNet to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, a naive Bayes approach learns semantic sense-based user profiles as binary text classifiers (user-likes and user-dislikes) from disambiguated documents. Experiments have been conducted to compare the performance obtained by keyword-based profiles to that obtained by sense-based profiles. Both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended have been considered. The main outcome is that the classification accuracy is increased with no improvement on the ranking. The conclusion is that the integration of linguistic knowledge in the learning process improves the classification of those documents whose classification score is close to the likes/dislikes threshold (the items for which the classification is highly uncertain).
---
paper_title: Collaborative user modeling for enhanced content filtering in recommender systems

paper_content:

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.
---
paper_title: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling

paper_content:

Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work proposes the integration of linguistic knowledge in the process of learning semantic user profiles that capture concepts concerning user interests. The proposed strategy consists of two steps. The first one is based on a word sense disambiguation technique that exploits the lexical database WordNet to select, among all the possible meanings (senses) of a polysemous word, the correct one. In the second step, a naive Bayes approach learns semantic sense-based user profiles as binary text classifiers (user-likes and user-dislikes) from disambiguated documents. Experiments have been conducted to compare the performance obtained by keyword-based profiles to that obtained by sense-based profiles. Both the classification accuracy and the effectiveness of the ranking imposed by the two different kinds of profile on the documents to be recommended have been considered. The main outcome is that the classification accuracy is increased with no improvement on the ranking. The conclusion is that the integration of linguistic knowledge in the learning process improves the classification of those documents whose classification score is close to the likes/dislikes threshold (the items for which the classification is highly uncertain).
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paper_title: ComSoc: adaptive transfer of user behaviors over composite social network

paper_content:

Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies in that, the available behavior data or interactions between users and items in a given social network are usually very limited and sparse (e.g., >= 99.9% empty). Many previous works model user behavior from only historical user logs. We observe that many people are members of several social networks in the same time, such as Facebook, Twitter and Tencent's QQ. Importantly, their behaviors and interests in different networks influence one another. This gives us an opportunity to leverage the knowledge of user behaviors in different networks, in order to alleviate the data sparsity problem, and enhance the predictive performance of user modeling. Combining different networks "simply and naively" does not work well. Instead, we formulate the problem to model multiple networks as "composite network knowledge transfer". We first select the most suitable networks inside a composite social network via a hierarchical Bayesian model, parameterized for individual users, and then build topic models for user behavior prediction using both the relationships in the selected networks and related behavior data. To handle big data, we have implemented the algorithm using Map/Reduce. We demonstrate that the proposed composite network-based user behavior model significantly improve the predictive accuracy over a number of existing approaches on several real world applications, such as a very large social-networking dataset from Tencent Inc.
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paper_title: Scalable distributed inference of dynamic user interests for behavioral targeting

paper_content:

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
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paper_title: TwitterRank: finding topic-sensitive influential twitterers

paper_content:

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.
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paper_title: A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

paper_content:

In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further "authoritative" tags to enrich his query and proposes them to him. All "authoritative" tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.
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paper_title: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

paper_content:

Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naive Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles.
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paper_title: A.: Twitris 2.0 : Semantically empowered system for understanding perceptions from social data

paper_content:

We present Twitris 2.0 , a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris 2.0 addresses challenges in large scale processing of social data, preserving spatiotemporal-thematic properties. Twitris 2.0 also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Semantic Web technologies enable the system’s integration and analysis abilities.
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paper_title: Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web

paper_content:

As the most popular microblogging platform, the vast amount of content on Twitter is constantly growing so that the retrieval of relevant information (streams) is becoming more and more difficult every day. Representing the semantics of individual Twitter activities and modeling the interests of Twitter users would allow for personalization and therewith countervail the information overload. Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well. However, automatically inferring the semantic meaning of Twitter posts is a non-trivial problem. ::: ::: In this paper we investigate semantic user modeling based on Twitter posts. We introduce and analyze methods for linking Twitter posts with related news articles in order to contextualize Twitter activities. We then propose and compare strategies that exploit the semantics extracted from both tweets and related news articles to represent individual Twitter activities in a semantically meaningful way. A large-scale evaluation validates the benefits of our approach and shows that our methods relate tweets to news articles with high precision and coverage, enrich the semantics of tweets clearly and have strong impact on the construction of semantic user profiles for the Social Web.
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paper_title: Short and tweet: experiments on recommending content from information streams

paper_content:

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.
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paper_title: Recommending twitter users to follow using content and collaborative filtering approaches

paper_content:

Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem -- for a given user, UT which other users might be recommended as followers/followees -- but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.
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paper_title: Collaborative user modeling for enhanced content filtering in recommender systems

paper_content:

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.
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paper_title: Building User Profile based on Concept and Relation for Web Personal ized Services

paper_content:

Ho w to capture and represent user interest is a key issue in personalized services for Web information seeking. This paper presents a method that builds and updates user profile based on semantics and browsing sequence. First, user profile is composed of concepts and relations, which can ensure the semantic representation of user interest. Secondly, user's browsing sequence of each Web page in a session is taken into account when the life time of concept and relation are computed. Thirdly, a memory model from cognitive psychology is introduced in updating concepts and relations in user profile after each session is finished, which ensures the dynamics of user profile. Experimental results indicate that this method is valid and effective in building and updating user profile. It can be seen that it has a brilliant perspective in the field of personalized services for Web information seeking.
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paper_title: Scalable distributed inference of dynamic user interests for behavioral targeting

paper_content:

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
---
paper_title: Scalable distributed inference of dynamic user interests for behavioral targeting

paper_content:

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
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paper_title: How People revisit Web Pages: empirical findings and implications for the design of history systems

paper_content:

We report on users' revisitation patterns to World Wide Web (web) pages, and use the results to lay an empirical foundation for the design of history mechanisms in web browsers. Through history, a user can return quickly to a previously visited page, possibly reducing the cognitive and physical overhead required to navigate to it from scratch. We analysed 6 weeks of detailed usage data collected from 23 users of a well-known web browser. We found that 58% of an individual's pages are revisits, and that users continually add new web pages into their repertoire of visited pages. People tend to revisit pages just visited, access only a few pages frequently, browse in very small clusters of related pages and generate only short sequences of repeated URL paths. We compared different history mechanisms, and found that the stack-based prediction method prevalent in commercial browsers is inferior to the simpler approach of showing the last few recently visited URLs with duplicates removed. Other predictive approaches fare even better. Based on empirical evidence, eight design guidelines for web browser history mechanisms were then formulated. When used to evaluate the existing hypertext-based history mechanisms, they explain why some aspects of today's browsers seem to work well, and other's poorly. The guidelines also indicate how history mechanisms in the web can be made even more effective.f1f1This article is a major expansion of a conference paper (Tauscher & Greenberg, 1997). This research reported in this article was performed as part of an M.Sc. project (Tauscher, 1996).
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Title: A Survey of User Modelling in Social Media Websites

Section 1: Introduction
Description 1: Describe the motivation and objectives of the paper, and provide a brief overview of the challenges and significance of user profiling in social media.

Section 2: Social Media
Description 2: Explain the role of social media in the digital age and categorize different types of social media websites based on their functionalities and user interactions.

Section 3: Issues and Problems
Description 3: Discuss the major issues and problems associated with user experience in social media that can be addressed through user profiling, such as data overload, dynamic interests, and business communication challenges.

Section 4: User Profiling in Social Media
Description 4: Define user profiling and describe its importance in social media, including the steps for building user profiles, and how user profiles are utilized in various applications.

Section 5: Data Sources
Description 5: Identify and describe the types of data sources used for user profiling, such as explicit data, implicit data, and social connections data.

Section 6: Keyword-Based User Profile Representation
Description 6: Explain different methods of representing user profiles using keywords, including vector representations and weighting mechanisms.

Section 7: User Profile Construction
Description 7: Provide an overview of various methodologies for constructing user profiles, including traditional bag-of-words, concepts-based, tag-based, and topics-based approaches.

Section 8: Semantic Enrichment
Description 8: Describe the process of semantic enrichment to enhance user profiles by using external sources such as WordNet, Wikipedia, web links, and data from socially connected users.

Section 9: Dynamic User Models
Description 9: Discuss the concept of dynamic user models and the methods to incorporate changes in user interests over time, including fading functions and adaptive profiles.

Section 10: Future Directions
Description 10: Highlight future research directions in user profiling in social media, focusing on increasing profile dynamicity, enhancing enrichment processes, and creating more comprehensive user profiles.

Section 11: Conclusion
Description 11: Summarize the findings of the paper, the current state of user profiling in social media, and suggest areas for further research.