A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques
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paper_title: Thinking Outside the (Search) Box

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Search is the main entry point for an ever-increasing range of information, services, communications and entertainment. During the last decade, there have been tremendous advances in the scale of search systems and the diversity of available resources. Yet the methods used to represent searchers' information needs have changed very little. Search interfaces today look much the same as they did a decade ago. Searchers type a few words into a search box, and the search engine returns a long list of results. When the results fail to satisfy the searcher's information needs, they try again and again with little support from the search engine. In this talk I describe several efforts to improve easy and effectiveness of search by: modeling searchers' interests and activities over time, representing non-content attributes of information such as time or genre, and developing interaction techniques that enable searchers to articulate their information needs more effectively.
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paper_title: Adaptive news access

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This chapter describes how the adaptive web technologies discussed in this book have been applied to news access. First, we provide an overview of different types of adaptivity in the context of news access and identify corresponding algorithms. For each adaptivity type, we briefly discuss representative systems that use the described techniques. Next, we discuss an in-depth case study of a personalized news system. As part of this study, we outline a user modeling approach specifically designed for news personalization, and present results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective. We conclude by discussing recent trends and novel systems in the adaptive news space.
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paper_title: 12 Hybrid Web Recommender Systems

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Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
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paper_title: Challenges for the Multi-dimensional Personalised Web

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Adaptive hypermedia and adaptive web research have been reasonable successful in researching personalisation in closed corpus content and to a much lesser extent in open corpus content. From a commercial perspective, web adaptivity has been more focused on adaptive content retrieval rather than adaptive content composition. However, personal use of the web extends far beyond just content, and encompasses many dimensions which need to be addressed concurrently e.g. tasks & activities, cultural preferences, and social interaction etc. We need to consider new directions and dimensions in personalised, adaptive web and how they can be addressed within the same personal experience. In this talk I will investigate key challenges involving integrated open corpus & service personalisation, cultural adaptivity (including multilingual personalisation), dialogue and simulation personalisation and the power of the crowd, which could greatly empower web users of the future. I will also consider emerging approaches to tackle these problems and examine what this might mean to current web based personalisation engines and platforms.
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paper_title: Collaborative filtering recommender systems

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One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.
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paper_title: Exploratory search: from finding to understanding

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Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.
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paper_title: Exploratory Search:Beyond the Query-Response Paradigm

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As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks
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paper_title: Homonymy And Polysemy In Information Retrieval

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This paper discusses research on distinguishing word meanings in the context of information retrieval systems. We conducted experiments with three sources of evidence for making these distinctions: morphology, part-of-speech, and phrases. We have focused on the distinction between homonymy and polysemy (unrelated vs. related meanings). Our results support the need to distinguish homonymy and polysemy. We found: 1) grouping morphological variants makes a significant improvement in retrieval performance, 2) that more than half of all words in a dictionary that differ in part-of-speech are related in meaning, and 3) that it is crucial to assign credit to the component words of a phrase. These experiments provide better understanding of word-based methods, and suggest where natural language processing can provide further improvements in retrieval performance.
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paper_title: Personalized search

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A contextual computing approach may prove a breakthrough in personalized search efficiency.
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paper_title: A survey and classification of semantic search approaches

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A broad range of approaches to semantic document retrieval has been developed in the context of the Semantic Web. This survey builds bridges among them. We introduce a classification scheme for semantic search engines and clarify terminology. We present an overview of ten selected approaches and compare them by means of our classification criteria. Based on this comparison, we identify not only common concepts and outstanding features, but also open issues. Finally, we give directions for future application development and research.
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paper_title: A Unified User Profile Framework for Query Disambiguation and Personalization

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Personalization of keyword searches has attracted interest in the re- search community as a means to decrease search ambiguity and return results that are more bound to be interesting to a particular user. We describe a term- based user profile that treats query disambiguation and personalization as a uni- form term rewriting process. Its key feature is the representation of connections between terms based on possible rewritings between them on a per user basis. We present a query-rewriting algorithm for such query disambiguation based on the proposed profiles. Preliminary experimental results show the potential of the overall approach.
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paper_title: Effective and efficient user interaction for long queries

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Handling long queries can involve either pruning the query to retain only the important terms (reduction), or expanding the query to include related concepts (expansion). While automatic techniques to do so exist, roughly 25% performance improvements in terms of MAP have been realized in past work through interactive variants. We show that selectively reducing or expanding a query leads to an average improvement of 51% in MAP over the baseline for standard TREC test collections. We demonstrate how user interaction can be used to achieve this improvement. Most interaction techniques present users with a fixed number of options for all queries. We achieve improvements by interacting less with the user, i.e., we present techniques to identify the optimal number of options to present to users, resulting in an interface with an average of 70% fewer options to consider. Previous algorithms supporting interactive reduction and expansion are exponential in nature. To extend their utility to operational environments, we present techniques to make the complexity of the algorithms polynomial. We finally present an analysis of long queries that continue to exhibit poor performance in spite of our new techniques.
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paper_title: Co-active intelligence for image retrieval

paper_content:

Lexical ambiguity in query-based image retrieval is an immemorial problem which has seemingly resisted all countermeasures. In this paper we introduce a methodology that expresses the users of a system and their navigational behaviour as the paramount resource for resolving query term ambiguity. Mass user consensus is modelled within a multi-dimensional feature space and evaluated through cluster analysis. This technique resolves query term ambiguity in a wholly democratic and dynamic fashion, in contrast to the brittle centralised models of contemporary word sense classification systems. The simple approach contained herein leads to several interesting emergent properties.
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paper_title: Web Search - Your Way

paper_content:

The World Wide Web is estimated at over 800 million indexable pages [9]. When searching the Web, a user can be overwhelmed by thousands of results retrieved by a search engine, few of which are valuable. The problem for search engines is not only to find relevant results, but results consistent with the user’s information need. It is a user’s information need that determines which documents are valuable. Typical search engines limit the user to entering only a keyword query, even though users consider more than the topic when making relevance judgments [1, 10].
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paper_title: A hybrid approach for searching in the semantic web

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This paper presents a search architecture that combines classical search techniques with spread activation techniques applied to a semantic model of a given domain. Given an ontology, weights are assigned to links based on certain properties of the ontology, so that they measure the strength of the relation. Spread activation techniques are used to find related concepts in the ontology given an initial set of concepts and corresponding initial activation values. These initial values are obtained from the results of classical search applied to the data associated with the concepts in the ontology. Two test cases were implemented, with very positive results. It was also observed that the proposed hybrid spread activation, combining the symbolic and the sub-symbolic approaches, achieved better results when compared to each of the approaches alone.
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paper_title: Improving personalized web search using result diversification

paper_content:

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.
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paper_title: Semantic annotation based exploratory search for information analysts

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The system presented in this article aims to improve information access through the use of semantic annotation utilizing a non-traditional approach. Instead of applying semantic annotations to enhance the internal information access mechanisms, we use them to empower the user of an information access system through an innovative named entity-based user interface - NameSieve. NameSieve was built to support an intelligence analyst during the process of exploratory search, an advanced type of search requiring multiple iterations of retrieval interleaved with browsing and analyzing the retrieved information. The proposed approach was implemented in the NameSieve system so that the system can transparently present a summary of search results in the form of entity ''clouds.'' Therefore, these clouds allow the analyst to further explore the results in a novel manner, acting together as a faceted browsing interface. We ran a user study (with ten subjects) to examine the effect of NameSieve, and the study results reported in the paper demonstrate that this new way of applying semantic annotation information was actively used and was evaluated positively by the subjects. It enabled the subjects to work more productively and bring back most relevant documents.
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paper_title: A heuristic-based methodology for semantic augmentation of user queries on the web

paper_content:

As the World Wide Web continues to grow, so does the need for effective approaches to processing users’ queries that retrieve the most relevant information. Most search engines provide the user with many web pages, but at varying levels of relevancy. The Semantic Web has been proposed to retrieve and use more semantic information from the web. However, the capture and processing of semantic information is a difficult task because of the well-known problems that machines have with processing semantics. This research proposes a heuristic-based methodology for building context aware web queries. The methodology expands a user’s query to identify possible word senses and then makes the query more relevant by restricting it using relevant information from the WordNet lexicon and the DARPA DAML library of domain ontologies. The methodology is implemented in a prototype. Initial testing of the prototype and comparison to results obtained from Google show that this heuristic based approach to processing queries can provide more relevant results to users, especially when query terms are ambiguous and/or when the methodology’s heuristics are invoked.
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paper_title: Personalized Ontology-Based Query Expansion

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In this paper we present a preliminary analysis of a new Google Desktop gadget aimed at defining personalised query expansions. This user friendly application is based on a dynamic local fuzzy ontology: every time that a user interacts with the gadget the ontology relationships are updated. The proposed query expansion technique takes into account past userpsilas queries and the concepts contained in relevant documents stored into the userpsilas workstation.
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paper_title: Personalized query expansion for the web

paper_content:

The inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to improve such Web queries by expanding them with terms collected from each user's Personal Information Repository, thus implicitly personalizing the search output. We introduce five broad techniques for generating the additional query keywords by analyzing user data at increasing granularity levels, ranging from term and compound level analysis up to global co-occurrence statistics, as well as to using external thesauri. Our extensive empirical analysis under four different scenarios shows some of these approaches to perform very well, especially on ambiguous queries, producing a very strong increase in the quality of the output rankings. Subsequently, we move this personalized search framework one step further and propose to make the expansion process adaptive to various features of each query. A separate set of experiments indicates the adaptive algorithms to bring an additional statistically significant improvement over the best static expansion approach.
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paper_title: Web Search - Your Way

paper_content:

The World Wide Web is estimated at over 800 million indexable pages [9]. When searching the Web, a user can be overwhelmed by thousands of results retrieved by a search engine, few of which are valuable. The problem for search engines is not only to find relevant results, but results consistent with the user’s information need. It is a user’s information need that determines which documents are valuable. Typical search engines limit the user to entering only a keyword query, even though users consider more than the topic when making relevance judgments [1, 10].
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paper_title: Web Search - Your Way

paper_content:

The World Wide Web is estimated at over 800 million indexable pages [9]. When searching the Web, a user can be overwhelmed by thousands of results retrieved by a search engine, few of which are valuable. The problem for search engines is not only to find relevant results, but results consistent with the user’s information need. It is a user’s information need that determines which documents are valuable. Typical search engines limit the user to entering only a keyword query, even though users consider more than the topic when making relevance judgments [1, 10].
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paper_title: Personalized query expansion for the web

paper_content:

The inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to improve such Web queries by expanding them with terms collected from each user's Personal Information Repository, thus implicitly personalizing the search output. We introduce five broad techniques for generating the additional query keywords by analyzing user data at increasing granularity levels, ranging from term and compound level analysis up to global co-occurrence statistics, as well as to using external thesauri. Our extensive empirical analysis under four different scenarios shows some of these approaches to perform very well, especially on ambiguous queries, producing a very strong increase in the quality of the output rankings. Subsequently, we move this personalized search framework one step further and propose to make the expansion process adaptive to various features of each query. A separate set of experiments indicates the adaptive algorithms to bring an additional statistically significant improvement over the best static expansion approach.
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paper_title: A personalized graph-based document ranking model using a semantic user profile

paper_content:

The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP) The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.
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paper_title: Persona: a contextualized and personalized web search

paper_content:

Recent advances in graph-based search techniques derived from Kleinberg's (1997) work have been impressive. This paper further improves the graph-based search algorithm in two dimensions. Firstly, variants of Kleinberg's techniques do not take into account the semantics of the query string nor of the nodes being searched. As a result, polysemy of query words cannot be resolved. This paper presents an interactive query scheme utilizing the simple Web taxonomy provided by the Open Directory Project to resolve meanings of a user query. Secondly, we extend a recently proposed personalized version of the Kleinberg algorithm (Chang et al., 2000). Simulation results are presented to illustrate the sensitivity of our technique. We outline the implementation of our algorithm in the Persona personalized web search system.
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paper_title: Search personalization through query and page topical analysis

paper_content:

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user's topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user's current query and the query-matching pages, in order to identify the user's current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.
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paper_title: Personalized Web search for improving retrieval effectiveness

paper_content:

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.
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paper_title: Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System

paper_content:

A case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTATM. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web.
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paper_title: Personalized search

paper_content:

A contextual computing approach may prove a breakthrough in personalized search efficiency.
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paper_title: Authoritative sources in a hyperlinked environment

paper_content:

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.
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paper_title: Context-aware ranking in web search

paper_content:

The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.
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paper_title: Search Engines - Information Retrieval in Practice

paper_content:

KEY BENEFIT: Written by a leader in the field of information retrieval, this text provides the background and tools needed to evaluate, compare and modify search engines. KEY TOPICS: Coverage of the underlying IR and mathematical models reinforce key concepts. Numerous programming exercises make extensive use of Galago, a Java-based open source search engine. MARKET: A valuable tool for search engine and information retrieval professionals.
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paper_title: Adaptation in Open Corpus Hypermedia

paper_content:

Adaptive hypermedia courseware benefits from being distributed over the Web: content can always be kept up-to-date, discussions and interactions between instructors and learners can be supported, new courses can easily be distributed to the students. Nevertheless, adaptive hypermedia systems are - even in the web context - still stand-alone systems as long as they lack the ability to integrate and adapt information from arbitrary places in the web. In this paper we discuss the concept and realization of an adaptive educational hypermedia system which facilitates adaptation of distributed learning materials. The proposed system is based on an indexing concept for various information resources and a Bayesian Network engine for calculating estimations on a user's changing knowledge. It implements constructivist teaching approaches, establishes conceptual models for designing courses and enables guided tours, intelligent example retrieval and further adaptive navigation support. Advantages and problems in designing adaptive open corpus hypermedia will be discussed in a concluding section.
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paper_title: Personal Readers : Personalized Learning Object Readers for the Semantic Web 1

paper_content:

This paper describes our idea for personalized e-Learning in the Semantic Web which is based on configurable, re-usable personalization services. To realize our ideas, we have developed a framework for designing, implementing and maintaining personal learning object readers, which enable the learners to study learning objects in an embedding, personalized context. We describe the architecture of our Personal Reader framework, and discuss the implementation of personalization services in the Semantic Web. We have realized two Personal Readers for e-Learning: one for learning Java programming, and another for learning about the Semantic Web.
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paper_title: GALE: a highly extensible adaptive hypermedia engine

paper_content:

This paper presents GALE, the GRAPPLE Adaptive Learning Environment, which (contrary to what the word suggests) is a truly generic and general purpose adaptive hypermedia engine. Five years have passed since "The Design of AHA!" was published at ACM Hypertext (2006). GALE takes the notion of general-purpose a whole lot further. We solve shortcomings of existing adaptive systems in terms of genericity, extensibility and usability and show how GALE improves on the state of the art in all these aspects. We illustrate different authoring styles for GALE, including the use of template pages, and show how adaptation can be defined in a completely decentralized way by using the open corpus adaptation facility of GALE. GALE has been used in a number of adaptive hypermedia workshops and assignments to test whether authors can actually make use of the extensive functionality that GALE offers. Adaptation has been added to wiki sites, existing material e.g. from w3schools, and of course also to locally authored hypertext. Soon GALE will be used in cross-course adaptation at the TU/e in a pilot project to improve the success rate of university students.
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paper_title: Personalization Using Ontologies and Rules

paper_content:

Adaptive hypermedia systems can alleviate information overload on the Web by personalising the delivery of resources to the user. These systems are however afflicted with difficulties in the acquisition of user data as well as the general lack of user control on and transparency of the systems' adaptive behavior. In this paper, we argue that the use of rules on top of ontologies can enable adaptive functionality that is both transparent and controllable for users. To this end, we sketch ODAS, a domain ontology for adaptive hypermedia systems, and a model for the specification of adaptation rules.
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paper_title: TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web

paper_content:

This paper presents TRIPLE, a layered and modular rule language for the Semantic Web [1]. TRIPLE is based on Horn logic and borrows many basic features from F-Logic [11] but is especially designed for querying and transforming RDF models [20].TRIPLE can be viewed as a successor of SiLRI (Simple Logic-based RDF Interpreter [5]). One of the most important differences to F-Logic and SiLRI is that TRIPLE does not have a fixed semantics for object-oriented features like classes and inheritance. Its layered architecture allows such features to be easily defined for different object-oriented and other data models like UML, Topic Maps, or RDF Schema [19]. Description logics extensions of RDF (Schema) like OIL [17] and DAML+OIL [3] that cannot be fully handled by Horn logic are provided as modules that interact with a description logic classifier, e.g. FaCT [9], resulting in a hybrid rule language. This paper sketches syntax and semantics of TRIPLE.
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paper_title: Dynamic assembly of learning objects

paper_content:

This paper describes one solution to the problem of how to select sequence, and link Web resources into a coherent, focused organization for instruction that addresses a user's immediate and focused learning need. A system is described that automatically generates individualized learning paths from a repository of XML Web resources. Each Web resource has an XML Learning Object Metadata (LOM) description consisting of General, Educational, and Classification metadata. Dynamic assembly of these learning objects is based on the relative match of the learning object content and metadata to the learner's needs, preferences, context, and constraints. Learning objects are connected into coherent paths based on their LOM topic classifications and the proximity of these topics in a Resource Description Framework (RDF) graph. An instructional sequencing policy specifies how to arrange the objects on the path into a particular learning sequence. The system has been deployed and evaluated within a corporate setting.
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paper_title: Semantic Search Meets the Web

paper_content:

While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.
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paper_title: An aspectual interface for supporting complex search tasks

paper_content:

With the increasing importance of search systems on the web, there is a continuing push to design interfaces which are a better match with the kinds of real-world tasks in which users are engaged. In this paper, we consider how broad, complex search tasks may be supported via the search interface. In particular, we consider search tasks which may be composed of multiple aspects, or multiple related subtasks. For example, in decision making tasks the user may investigate multiple possible solutions before settling on a single, final solution, while other tasks, such as report writing, may involve searching on multiple interrelated topics. A search interface is presented which is designed to support such broad search tasks, allowing a user to create search aspects, each of which models an independent subtask of some larger task. The interface is built on the intuition that users should be able to structure their searching environment when engaged on complex search tasks, where the act of structuring and organization may aid the user in understanding his or her task. A user study was carried out which compared our aspectual interface to a standard web-search interface. The results suggest that an aspectual interface can aid users when engaged in broad search tasks where the search aspects must be identified during searching; for a task where search aspects were pre-defined, no advantage over the baseline was found. Results for a decision making task were less clear cut, but show some evidence for improved task performance.
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paper_title: Strategy hubs: next-generation domain portals with search procedures

paper_content:

Current search tools on the Web, such as general-purpose search engines (e.g. Google) and domain-specific portals (e.g. MEDLINEplus), do not provide search procedures that guide users to form appropriately ordered sub-goals. The lack of such procedural knowledge often leads users searching in unfamiliar domains to retrieve incomplete information. In critical domains such as in healthcare, such ineffective searches can have dangerous consequences. To address this situation, we developed a new type of domain portal called a Strategy Hub. Strategy Hubs provide the critical search procedures and associated high-quality links that enable users to find comprehensive and accurate information. This paper describes how we collaborated with skin cancer physicians to systematically identify generalizeable search procedures to find comprehensive information about melanoma, and how these search procedures were made available through the Strategy Hub for healthcare. A pilot study suggests that this approach can improve the efficacy, efficiency, and satisfaction of even expert searchers. We conclude with insights on how to refine the design of the Strategy Hub, and how it can be used to provide search procedures across domains.
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paper_title: Evaluating interfaces for government metasearch

paper_content:

Metasearch tools, which combine search results from any number of independent search engines, could be useful in a range of tasks and especially for integrating information from separate government agencies. However, it is not immediately clear what user interfaces might be appropriate for presenting results from more than one source. We evaluated four interface designs with real tasks from Centrelink, Australia's social services agency, and with a working metasearch tool. Test users recorded similar overall effectiveness across these interfaces, but did not like the most familiar options, a single ranked list or a link to broaden search scope. Interfaces which supported identifying, understanding, and selecting between sources were strongly preferred.
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paper_title: Co-active intelligence for image retrieval

paper_content:

Lexical ambiguity in query-based image retrieval is an immemorial problem which has seemingly resisted all countermeasures. In this paper we introduce a methodology that expresses the users of a system and their navigational behaviour as the paramount resource for resolving query term ambiguity. Mass user consensus is modelled within a multi-dimensional feature space and evaluated through cluster analysis. This technique resolves query term ambiguity in a wholly democratic and dynamic fashion, in contrast to the brittle centralised models of contemporary word sense classification systems. The simple approach contained herein leads to several interesting emergent properties.
---
paper_title: Web Search - Your Way

paper_content:

The World Wide Web is estimated at over 800 million indexable pages [9]. When searching the Web, a user can be overwhelmed by thousands of results retrieved by a search engine, few of which are valuable. The problem for search engines is not only to find relevant results, but results consistent with the user’s information need. It is a user’s information need that determines which documents are valuable. Typical search engines limit the user to entering only a keyword query, even though users consider more than the topic when making relevance judgments [1, 10].
---
paper_title: Improving personalized web search using result diversification

paper_content:

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.
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paper_title: A task-oriented study on the influencing effects of query-biased summarisation in web searching q

paper_content:

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web.To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare the effectiveness of two of the web's major search engines; Alta Vista and Google.
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paper_title: Dynamic assembly of personalized learning content on the semantic web

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This paper presents an ontology-based approach for automatic decomposition of learning objects (LOs) into reusable content units, and dynamic reassembly of such units into personalized learning content. To test our approach we developed TANGRAM, an integrated learning environment for the domain of Intelligent Information Systems. Relying on a number of ontologies, TANGRAM allows decomposition of LOs into smaller content units, which can be later assembled into new LOs personalized to the user's domain knowledge, preferences, and learning styles. The focus of the presentation is on the ontologies themselves, in the context of user modeling and personalization. Furthermore, the paper presents the algorithm we apply to dynamically assemble content units into personalized learning content. We also discuss our experiences with dynamic content generation and point out directions for future work.
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paper_title: Adaptive faceted browser for navigation in open information spaces

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Open information spaces have several unique characteristics such as their changeability, large size, complexity and diverse user base. These result in novel challenges during user navigation, information retrieval and data visualization in open information spaces.We propose a method of navigation in open information spaces based on an enhanced faceted browser with support for dynamic facet generation and adaptation based on user characteristics.
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paper_title: Semantic Composition of Lecture Subparts for a Personalized e-Learning

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In this paper we propose an algorithm for personalized learning based on a user's query and a repository of lecture subparts --i.e., learning objects-- both are described in a subset of OWL-DL. It works in two steps. First, it retrieves lecture subparts that cover as much as possible the user's query. The solution is based on the concept covering problem for which we present a modified algorithm. Second, an appropriate sequence of lecture subparts is generated. Indeed, the different lecture subparts are only reachable when a given prerequisite is fulfilled, i.e., the learner must have a minimal background knowledge to be able to assimilate the requested learning object. Therefore, our algorithm takes into account the user's knowledge to generate a personalized lecture composition and suggests a flow of learning objects to the user.
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paper_title: Interactive retrieval based on faceted feedback

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Motivated by the commonly used faceted search interface in e-commerce, this paper investigates interactive relevance feedback mechanism based on faceted document metadata. In this mechanism, the system recommends a group of document facet-value pairs, and lets users select relevant ones to restrict the returned documents. We propose four facet-value pair recommendation approaches and two retrieval models that incorporate user feedback on document facets. Evaluated based on user feedback collected through Amazon Mechanical Turk, our experimental results show that the Boolean filtering approach, which is widely used in faceted search in e-commerce, doesn't work well for text document retrieval, due to the incompleteness (low recall) of metadata assignment in semi-structured text documents. Instead, a soft model performs more effectively. The faceted feedback mechanism can also be combined with document-based relevance feedback and pseudo relevance feedback to further improve the retrieval performance.
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paper_title: The value of adaptive link annotation in e-learning: a study of a portal-based approach

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Adaptive link annotation is one of the most popular adaptive educational hypermedia techniques. It has been widely studied and demonstrated its ability to help students to acquire knowledge faster, improve learning outcomes, reduce navigation overhead, increase motivation, and encourage the beneficial non-sequential navigation. However, almost all studies of adaptive link annotation have been performed in the context of dedicated adaptive educational hypermedia systems. The role of this technique in the context of widely popular learning portals has not yet been demonstrated. In this paper, we attempt to fill this gap by investigating the value of adaptive navigation support embedded into the learning portal. We compare the effect of portal-based adaptive navigation support to both the effect of the adaptive navigation support in adaptive educational hypermedia systems and to non-adaptive learning portals.
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paper_title: Adaptation in Open Corpus Hypermedia

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Adaptive hypermedia courseware benefits from being distributed over the Web: content can always be kept up-to-date, discussions and interactions between instructors and learners can be supported, new courses can easily be distributed to the students. Nevertheless, adaptive hypermedia systems are - even in the web context - still stand-alone systems as long as they lack the ability to integrate and adapt information from arbitrary places in the web. In this paper we discuss the concept and realization of an adaptive educational hypermedia system which facilitates adaptation of distributed learning materials. The proposed system is based on an indexing concept for various information resources and a Bayesian Network engine for calculating estimations on a user's changing knowledge. It implements constructivist teaching approaches, establishes conceptual models for designing courses and enables guided tours, intelligent example retrieval and further adaptive navigation support. Advantages and problems in designing adaptive open corpus hypermedia will be discussed in a concluding section.
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paper_title: Adaptive Navigation Support for Parameterized Questions in Object-Oriented Programming

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This paper explores the impact of adaptive navigation support on student work with parameterized questions in the domain of object-oriented programming. In the past, we developed QuizJET system, which is able to generate and assess parameterized Java programming questions. More recently, we developed JavaGuide system, which enhances QuizJET questions with adaptive navigation support. This system introduces QuizJET and JavaGuide and reports the results of classroom studies, which explored the impact of these systems and assessed an added value of adaptive navigation support. The results of the studies indicate that adaptive navigation support encourages students use parameterized questions more extensively. Students are also 2.5 times more likely to answer parameterized questions correctly with adaptive navigation support than without such support. In addition, we found that adaptive navigation support especially benefit weaker students helping to close the gap between strong and weak students.
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paper_title: Personal Readers : Personalized Learning Object Readers for the Semantic Web 1

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This paper describes our idea for personalized e-Learning in the Semantic Web which is based on configurable, re-usable personalization services. To realize our ideas, we have developed a framework for designing, implementing and maintaining personal learning object readers, which enable the learners to study learning objects in an embedding, personalized context. We describe the architecture of our Personal Reader framework, and discuss the implementation of personalization services in the Semantic Web. We have realized two Personal Readers for e-Learning: one for learning Java programming, and another for learning about the Semantic Web.
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paper_title: GALE: a highly extensible adaptive hypermedia engine

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This paper presents GALE, the GRAPPLE Adaptive Learning Environment, which (contrary to what the word suggests) is a truly generic and general purpose adaptive hypermedia engine. Five years have passed since "The Design of AHA!" was published at ACM Hypertext (2006). GALE takes the notion of general-purpose a whole lot further. We solve shortcomings of existing adaptive systems in terms of genericity, extensibility and usability and show how GALE improves on the state of the art in all these aspects. We illustrate different authoring styles for GALE, including the use of template pages, and show how adaptation can be defined in a completely decentralized way by using the open corpus adaptation facility of GALE. GALE has been used in a number of adaptive hypermedia workshops and assignments to test whether authors can actually make use of the extensive functionality that GALE offers. Adaptation has been added to wiki sites, existing material e.g. from w3schools, and of course also to locally authored hypertext. Soon GALE will be used in cross-course adaptation at the TU/e in a pilot project to improve the success rate of university students.
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paper_title: Towards Ontology-Driven Discourse: From Semantic Graphs to Multimedia Presentations

paper_content:

Traditionally, research in applying Semantic Web technology to multimedia information systems has focused on using annotations and ontologies to improve the retrieval process. This paper concentrates on improving the presentation of the retrieval results. ::: ::: First, our approach uses ontological domain knowledge to select and organize the content relevant to the topic the user is interested in. Domain ontologies are valuable in the presentation generation process, because effective presentations are those that succeed in conveying the relevant domain semantics to the user. Explicit discourse and narrative knowledge allows selection of appropriate presentation genres and creation of narrative structures, which are used for conveying these domain relations. ::: ::: In addition, knowledge of graphic design and media characteristics is essential to transform abstract presentation structures into real multimedia presentations. Design knowledge determines how the semantics and presentation structure are expressed in the multimedia presentation. In traditional Web environments, this type of design knowledge remains implicit, hidden in style sheets and other document transformation code. Our second use of Semantic Web technology is to model design knowledge explicitly, and to enable it to drive the transformations needed to turn annotated media items into structured presentations.
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paper_title: Challenges for Supporting Faceted Search in Large, Heterogeneous Corpora like the Web

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Faceted search systems help people find what they are looking by allowing them to specify not just keywords related to their information need, but also metadata. While such systems hold great potential and have been successfully used in vertical domains, there are many challenges in extending them to large, heterogeneous collections like the Web, corporate intranets, or federated search engines that access many different data silos. In this position paper we discuss the challenges in greater detail. Those that we have identified stem from the fact that such datasets are 1) very large, making it difficult to assign quality meta-data to every document and to retrieve the full set of results and associated metadata at query time, and 2) heterogeneous, making it difficult to apply the same metadata to every result or every query.
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paper_title: Dynamic assembly of learning objects

paper_content:

This paper describes one solution to the problem of how to select sequence, and link Web resources into a coherent, focused organization for instruction that addresses a user's immediate and focused learning need. A system is described that automatically generates individualized learning paths from a repository of XML Web resources. Each Web resource has an XML Learning Object Metadata (LOM) description consisting of General, Educational, and Classification metadata. Dynamic assembly of these learning objects is based on the relative match of the learning object content and metadata to the learner's needs, preferences, context, and constraints. Learning objects are connected into coherent paths based on their LOM topic classifications and the proximity of these topics in a Resource Description Framework (RDF) graph. An instructional sequencing policy specifies how to arrange the objects on the path into a particular learning sequence. The system has been deployed and evaluated within a corporate setting.
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paper_title: Faceted metadata for image search and browsing

paper_content:

There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.
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paper_title: Co-active intelligence for image retrieval

paper_content:

Lexical ambiguity in query-based image retrieval is an immemorial problem which has seemingly resisted all countermeasures. In this paper we introduce a methodology that expresses the users of a system and their navigational behaviour as the paramount resource for resolving query term ambiguity. Mass user consensus is modelled within a multi-dimensional feature space and evaluated through cluster analysis. This technique resolves query term ambiguity in a wholly democratic and dynamic fashion, in contrast to the brittle centralised models of contemporary word sense classification systems. The simple approach contained herein leads to several interesting emergent properties.
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paper_title: IntrospectiveViews: an interface for scrutinizing semantic user models

paper_content:

User models are a key component for user-adaptive systems They represent information about users such as interests, expertise, goals, traits, etc This information is used to achieve various adaptation effects, e.g., recommending relevant documents or products To ensure acceptance by users, these models need to be scrutable, i.e., users must be able to view and alter them to understand and if necessary correct the assumptions the system makes about the user However, in most existing systems, this goal is not met In this paper, we introduce IntrospectiveViews, an interface that enables the user to view and edit her user model Furthermore, we present the results of a formative evaluation that show the importance users give in general to different aspects of scrutable user models and also substantiate our claim that IntrospectiveViews is an appropriate realization of an interface to such models.
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paper_title: Context-aware ranking in web search

paper_content:

The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.
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paper_title: Towards Open Corpus Adaptive Hypermedia: A Study of Novelty Detection Approaches

paper_content:

Classic adaptive hypermedia systems are able to track a user's knowledge of the subject and use it to evaluate the novelty and difficulty of content encountered by the user. Our goal is to implement this functionality in an open corpus context where a domain model is not available nor is the content indexed with domain concepts. We examine methods for novelty measurement based on automatic text analysis. To compare these methods, we use an evaluation approach based on knowledge encapsulated in the structure of a textbook. Our study shows that a knowledge accumulation method adopted from the domain of intelligent tutoring systems offers a more meaningful novelty measurement than methods adapted from the area of personalized information retrieval.
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paper_title: Natural language question answering: the view from here

paper_content:

As users struggle to navigate the wealth of on-line information now available, the need for automated question answering systems becomes more urgent. We need systems that allow a user to ask a question in everyday language and receive an answer quickly and succinctly, with sufficient context to validate the answer. Current search engines can return ranked lists of documents, but they do not deliver answers to the user.Question answering systems address this problem. Recent successes have been reported in a series of question-answering evaluations that started in 1999 as part of the Text Retrieval Conference (TREC). The best systems are now able to answer more than two thirds of factual questions in this evaluation.
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paper_title: UPS: efficient privacy protection in personalized web search

paper_content:

In recent years, personalized web search (PWS) has demonstrated effectiveness in improving the quality of search service on the Internet. Unfortunately, the need for collecting private information in PWS has become a major barrier for its wide proliferation. We study privacy protection in PWS engines which capture personalities in user profiles. We propose a PWS framework called UPS that can generalize profiles in for each query according to user-specified privacy requirements. Two predictive metrics are proposed to evaluate the privacy breach risk and the query utility for hierarchical user profile. We develop two simple but effective generalization algorithms for user profiles allowing for query-level customization using our proposed metrics. We also provide an online prediction mechanism based on query utility for deciding whether to personalize a query in UPS. Extensive experiments demonstrate the efficiency and effectiveness of our framework.
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paper_title: Personal Services: Debating the Wisdom of Personalisation

paper_content:

Personalisation has been seen as the answer to the vast quantity of information now available digitally. Information is tailored to meet each user's apparent needs, whether making use of Web-based learning materials, purchasing from online sites, accessing entertainment products or any other potentially-personalisable facility. Yet before rushing headlong into personalising everything, including online learning systems, we need to consider the threats posed by the capture of so much data about individuals, the implications of personalised information delivery, and whether the benefits of personalisation justify the costs. In this paper, we look at both the issues and benefits of personalisation, and consider how to steer our way to a safe and valuable use of personalisation.
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paper_title: Accurately Interpreting Clickthrough Data as Implicit Feedback

paper_content:

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.
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paper_title: Adaptive news access

paper_content:

This chapter describes how the adaptive web technologies discussed in this book have been applied to news access. First, we provide an overview of different types of adaptivity in the context of news access and identify corresponding algorithms. For each adaptivity type, we briefly discuss representative systems that use the described techniques. Next, we discuss an in-depth case study of a personalized news system. As part of this study, we outline a user modeling approach specifically designed for news personalization, and present results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective. We conclude by discussing recent trends and novel systems in the adaptive news space.
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paper_title: Improving personalized web search using result diversification

paper_content:

We present and evaluate methods for diversifying search results to improve personalized web search. A common personalization approach involves reranking the top N search results such that documents likely to be preferred by the user are presented higher. The usefulness of reranking is limited in part by the number and diversity of results considered. We propose three methods to increase the diversity of the top results and evaluate the effectiveness of these methods.
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paper_title: Personal Services: Debating the Wisdom of Personalisation

paper_content:

Personalisation has been seen as the answer to the vast quantity of information now available digitally. Information is tailored to meet each user's apparent needs, whether making use of Web-based learning materials, purchasing from online sites, accessing entertainment products or any other potentially-personalisable facility. Yet before rushing headlong into personalising everything, including online learning systems, we need to consider the threats posed by the capture of so much data about individuals, the implications of personalised information delivery, and whether the benefits of personalisation justify the costs. In this paper, we look at both the issues and benefits of personalisation, and consider how to steer our way to a safe and valuable use of personalisation.
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paper_title: Automatic mining of cognitive metadata using fuzzy inference

paper_content:

Personalized search and browsing is increasingly vital especially for enterprises to able to reach their customers. Key challenge in supporting personalization is the need for rich metadata such as cognitive metadata about documents. As we consider size of large knowledge bases, manual annotation is not scalable and feasible. On the other hand, automatic mining of cognitive metadata is challenging since it is very difficult to understand underlying intellectual knowledge about documents automatically. To alleviate this problem, we introduce a novel metadata extraction framework, which is based on fuzzy information granulation and fuzzy inference system for automatic cognitive metadata mining. The user evaluation study shows that our approach provides reasonable precision rates for difficulty, interactivity type, and interactivity level on the examined 100 documents. In addition, proposed fuzzy inference system achieves improved results compared to a rule-based reasoner for document difficulty metadata extraction (11% improvement).
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Title: A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

Section 1: Introduction
Description 1: Introduce the main topic of the paper, discussing the problem of information overload and the development of Personalised Information Retrieval (PIR) and Adaptive Hypermedia (AH) techniques to address it.

Section 2: Information Access Paradigms of PIR and AH
Description 2: Explain the different information access paradigms of PIR and AH, detailing how each approach deals with user queries and the retrieval of relevant information.

Section 3: Query adaptation
Description 3: Describe various techniques for adapting user queries in PIR and AH, including methods like query expansion, trimming, and substitution to better reflect users' informational intentions.

Section 4: Retrieval adaptation
Description 4: Discuss methods for adapting the retrieval process in both PIR and AH, highlighting statistical methods and metadata-based approaches that tailor retrieval to provide more relevant results for specific users.

Section 5: Adaptive Composition & Presentation
Description 5: Explore techniques for composing and presenting retrieved information in a personalized manner, including clustering, summarization, faceted rankings, and adaptive navigation and guidance strategies.

Section 6: Comparison across Query Adaptation techniques
Description 6: Compare and critique the various query adaptation techniques used in PIR and AH, evaluating their effectiveness and limitations.

Section 7: Comparison across Retrieval Adaptation techniques
Description 7: Analyze and contrast different retrieval adaptation techniques used in PIR and AH, discussing their scalability and the richness of user and domain models.

Section 8: Comparison across Adaptive Composition & Presentation techniques
Description 8: Compare methods of adaptive composition and presentation used in PIR and AH, considering their applicability, metadata requirements, and the extent of personalisation they provide.

Section 9: Conclusions
Description 9: Summarize the overall findings of the survey regarding the strengths and weaknesses of PIR and AH techniques, and outline the potential benefits of a hybridised approach that combines elements from both fields.

Section 10: Hybridisation
Description 10: Investigate the potential for combining PIR and AH techniques into a hybrid approach, detailing how such a system could be structured and the specific stages where integration would be beneficial.

Section 11: Application scenario
Description 11: Provide a real-world example of how a hybrid PIR-AH system could be applied, such as in a customer support information portal, highlighting the specific advantages and potential impact.

Section 12: Future Directions
Description 12: Discuss the remaining challenges and opportunities for future research in the field, including issues of privacy, data confidentiality, and the incorporation of social aspects into personalized information systems.