BibSonomy publications for /user/jaeschke/recommenderWed Feb 10 17:06:42 CET 2010Tilburg, The NetherlandsdecRecommender Systems for Social Bookmarking2009bookmarking folksonomy item recommender social tagging Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. Wed Dec 16 11:44:28 CET 2009ECML PKDD Discovery Challenge 2009 (DC09)eisterlehner2009ecmlpkddsep157--172CEUR-WS.orgTag Sources for Recommendation in Collaborative Tagging Systems4972009dc09 recommender tag Wed Dec 16 11:42:31 CET 2009eisterlehner2009ecmlpkddSeptember35--48CEUR-WS.orgSocial Tag Prediction Base on Supervised Ranking Model4972009dc09 recommender tag Wed Dec 16 11:42:31 CET 2009eisterlehner2009ecmlpkddSeptember243--260CEUR-WS.orgContent-based and Graph-based Tag Suggestion4972009dc09 recommender tag Thu Dec 10 16:56:57 CET 2009Workshop on Knowledge Discovery, Data Mining, and Machine Learningsep44--51Testing and Evaluating Tag Recommenders in a Live System20092009 bibsonomy folksonomy framework myown recommender tagging The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.Thu Dec 10 16:56:08 CET 2009New York, NY, USARecSys '09: Proceedings of the third ACM Conference on Recommender Systems369--372Testing and Evaluating Tag Recommenders in a Live System20092009 bibsonomy conference framework myown recommender recsys The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a rst evaluation of two exemplarily deployed recommendation methods.Tue Nov 17 11:37:31 CET 2009New York, NY, USAWWW '04: Proceedings of the 13th International Conference on World Wide Web393--402Shilling recommender systems for fun and profit2004recommender spam Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.Tue Nov 17 11:11:59 CET 2009New York, NY, USAIUI '09: Proceedings of the 13th International Conference on Intelligent User Interfaces47--56Tagsplanations: explaining recommendations using tags2009explaining recommender tagging While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.Tue Nov 17 11:02:14 CET 2009New York, NY, USACSCW '00: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work241--250Explaining collaborative filtering recommendations2000cf collaborative explaining filtering recommender Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.Sun Nov 15 12:59:53 CET 2009New York, NY, USANSPW '97: Proceedings of the 1997 Workshop on New Security Paradigms48--60A distributed trust model1997model recommender trust The widespread use of the Internet signals the need for a better understanding of trust as a basis for secure on-line interaction. In the face of increasing uncertainty and risk, users must be allowed to reason effectively about the trustworthiness of on-line entities. In this paper, we outline the shortcomings of current security approaches for managing trust and propose a model for trust, based on distributed recommendations.Sun Nov 15 12:42:30 CET 2009New York, NY, USAIUI '05: Proceedings of the 10th International Conference on Intelligent User Interfaces167--174Trust in recommender systems2005recommender trust Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by leveraging the preferences of communities of similar users. In this paper we suggest that the traditional emphasis on user similarity may be overstated. We argue that additional factors have an important role to play in guiding recommendation. Specifically we propose that the trustworthiness of users must be an important consideration. We present two computational models of trust and show how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways. We also show how these trust models can lead to improved predictive accuracy during recommendation.Sun Nov 15 12:26:34 CET 2009Berlin/HeidelbergOn the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops894--903Lecture Notes in Computer ScienceA Classification of Trust Systems.42772006recommender system trust Trust is a promising research topic for social networks, since it is a basic component of our real-world social life. Yet, the transfer of the multi-facetted concept of trust to virtual social networks is an open challenge. In this paper we provide a survey and classification of established and upcoming trust systems, focusing on trust models. We introduce a set of criteria as basis of our analysis and show strengths and short-comings of the different approaches.Tue Nov 10 11:38:39 CET 2009Berlin/HeidelbergThe Semantic Web: Research and Applications486--499Lecture Notes in Computer ScienceCollaborative and Usage-Driven Evolution of Personal Ontologies.35322005ontology recommender semantic web Large information repositories as digital libraries, online shops, etc. rely on a taxonomy of the objects under consideration to structure the vast contents and facilitate browsing and searching (e.g., ACM topic classification for computer science literature, Amazon product taxonomy, etc.). As in heterogenous communities users typically will use different parts of such an ontology with varying intensity, customization and personalization of the ontologies is desirable. Of particular interest for supporting users during the personalization are collaborative filtering systems which can produce personal recommendations by computing the similarity between own preferences and the one of other people. In this paper we adapt a collaborative filtering recommender system to assist users in the management and evolution of their personal ontology by providing detailed suggestions of ontology changes. Such a system has been implemented in the context of Bibster, a peer-to-peer based personal bibliography management tool. Finally, we report on an experiment with the Bibster community that shows the performance improvements over non-personalized recommendations.Fri Oct 30 15:03:49 CET 2009submitted for publicationRecommending Semantic Annotations for Geographic Information2009annotation geo gis recommender semantic Fri Oct 30 11:37:58 CET 2009New York, NY, USACHI '03: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems585--592Is seeing believing?: how recommender system interfaces affect users' opinions2003influence opinion recommender Recommender systems use people's opinions about items in an information domain to help people choose other items. These systems have succeeded in domains as diverse as movies, news articles, Web pages, and wines. The psychological literature on conformity suggests that in the course of helping people make choices, these systems probably affect users' opinions of the items. If opinions are influenced by recommendations, they might be less valuable for making recommendations for other users. Further, manipulators who seek to make the system generate artificially high or low recommendations might benefit if their efforts influence users to change the opinions they contribute to the recommender. We study two aspects of recommender system interfaces that may affect users' opinions: the rating scale and the display of predictions at the time users rate items. We find that users rate fairly consistently across rating scales. Users can be manipulated, though, tending to rate toward the prediction the system shows, whether the prediction is accurate or not. However, users can detect systems that manipulate predictions. We discuss how designers of recommender systems might react to these findings.Fri Oct 16 10:34:04 CEST 2009Washington, DC, USAICDE '09: Proceedings of the 2009 IEEE International Conference on Data Engineering1467--1470Flexible Recommendations for Course Planning2009course recommender webzub Most recommendation methods are "hard-wired" into the system and support only fixed recommendations. The purpose of this demo is to show the expressivity of flexible recommendation workflows, how flexible recommendations can be processed over relational data, and to show flexible recommendations in action through a real system used for course planning.Mon Jun 29 11:13:35 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyThe Role of Tag Suggestions in Folksonomies2009folksonomy ht09 impact recommender tag Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.Mon Jun 29 11:01:24 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulySocial Recommender Systems for Web 2.0 Folksonomies2009folksonomy recommender social The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.Fri Jun 26 15:34:40 CEST 2009Enschede, The NetherlandsdecSupporting people in finding information : hybrid recommender systems and goal-based structuring2005hybrid recommender The Internet has provided people with the possibility to easily publish and search for information. This resulted in an enormous amount of available information, products and services that also made it a challenge to find that what is really interesting to a person. Finding something really interesting is like searching for the proverbial needle in a haystack. This thesis addresses solutions to support people in finding interesting items by focusing on information systems that automatically learn and adapt their behaviour in order to support their users. The solutions provided in this thesis correspond to the three main processes in personalized information systems: selecting, structuring and presenting items. Tue Jun 23 08:43:15 CEST 2009Hingham, MA, USAUser Modeling and User-Adapted Interactionnov4331--370Hybrid Recommender Systems: Survey and Experiments122002hybrid recommender survey Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.