BibSonomyburst/group/iteg/recommenderBibSonomy RSS feed for /group/iteg/recommender2012-02-16T13:00:45+01:00http://www.bibsonomy.org/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">L. Balby Marinho</a>, <a href="/author/Hotho">A. Hotho</a>, <a href="/author/Jäschke">R. Jäschke</a>, <a href="/author/Nanopoulos">A. Nanopoulos</a>, <a href="/author/Rendle">S. Rendle</a>, <a href="/author/Schmidt-Thieme">L. Schmidt-Thieme</a>, <a href="/author/Stumme">G. Stumme</a>, and <a href="/author/Symeonidis">P. Symeonidis</a> </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em>Springer, </em>(<em>February 2012</em>)Tue Feb 14 08:29:50 CET 2012febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy myown recommender social tagging Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/hothohotho2012-02-09T09:26:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Thu Feb 09 09:26:57 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/stummestumme2012-02-06T14:59:32+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Mitzlaff">Folke Mitzlaff</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Recommender Systems for the Social Web, </em><em>volume 32 of Intelligent Systems Reference Library, </em><em>Springer, </em><em>Berlin/Heidelberg, </em>(<em>2012</em>)Mon Feb 06 14:59:32 CET 2012Berlin/HeidelbergRecommender Systems for the Social Web65--87Intelligent Systems Reference LibraryChallenges in Tag Recommendations for Collaborative Tagging Systems3220122012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.http://www.bibsonomy.org/bibtex/2708be7b5c269bd3a9d3d2334f858d52d/stummestumme2012-01-04T11:31:34+01:002011 collaborative itegpub myown recommender social tagging <span class="authorEditorList"><a href="/author/Balby Marinho">Leandro Balby Marinho</a>, <a href="/author/Nanopoulos">Alexandros Nanopoulos</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Stumme">Gerd Stumme</a>, and <a href="/author/Symeonidis">Panagiotis Symeonidis</a> </span><em>Recommender Systems Handbook, </em><em>Springer, </em><em>New York, </em>(<em>2011</em>)Wed Jan 04 11:31:34 CET 2012New YorkRecommender Systems Handbook615--644Social Tagging Recommender Systems20112011 collaborative itegpub myown recommender social tagging The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the noise that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.http://www.bibsonomy.org/bibtex/241dbb2c9f71440c9aa402f8966117979/hothohotho2011-11-30T14:46:32+01:002011 collaborative myown recommender social tagging taggingsurvey web <span class="authorEditorList"><a href="/author/Burke">Robin Burke</a>, <a href="/author/Gemmell">Jonathan Gemmell</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Jäschke">Robert Jäschke</a> </span><em>AI Magazine</em> <em>32(3):46--56</em> (<em>2011</em>)Wed Nov 30 14:46:32 CET 2011AI Magazine346--56Recommendation in the Social Web3220112011 collaborative myown recommender social tagging taggingsurvey web Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.http://www.bibsonomy.org/bibtex/2f9d6e06ab0f2fdcebb77afa97d72e40a/folkefolke2011-11-28T18:28:50+01:00folksonomy recommender tag <span class="authorEditorList"><a href="/author/Illig">Jens Illig</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Knowledge Processing and Data Analysis, </em><em>volume 6581 of Lecture Notes in Computer Science, </em><em>page 136--149. </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>2011</em>)Mon Nov 28 18:28:50 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems65812011folksonomy recommender tag Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. http://www.bibsonomy.org/bibtex/2f9d6e06ab0f2fdcebb77afa97d72e40a/hothohotho2011-11-25T12:41:14+01:002011 content folksonomy myown recommendations recommender tag <span class="authorEditorList"><a href="/author/Illig">Jens Illig</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Knowledge Processing and Data Analysis, </em><em>volume 6581 of Lecture Notes in Computer Science, </em><em>page 136--149. </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>2011</em>)Fri Nov 25 12:41:14 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy myown recommendations recommender tag Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. http://www.bibsonomy.org/bibtex/2f9d6e06ab0f2fdcebb77afa97d72e40a/stummestumme2011-11-25T12:41:06+01:002011 content folksonomy itegpub l3s myown recommendations recommender tag tagorapub <span class="authorEditorList"><a href="/author/Illig">Jens Illig</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Knowledge Processing and Data Analysis, </em><em>volume 6581 of Lecture Notes in Computer Science, </em><em>page 136--149. </em><em>Berlin/Heidelberg, </em><em>Springer, </em>(<em>2011</em>)Fri Nov 25 12:41:06 CET 2011Berlin/HeidelbergKnowledge Processing and Data Analysis136--149Lecture Notes in Computer ScienceA Comparison of Content-Based Tag Recommendations in Folksonomy Systems658120112011 content folksonomy itegpub l3s myown recommendations recommender tag tagorapub Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. http://www.bibsonomy.org/bibtex/21ac91a922a872523de0ce8d4984e53a3/hothohotho2011-11-16T16:51:09+01:00bibsonomy bookmarking dissertation fca recommender social tag tagging taggingsurvey <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a> </span><em>Amsterdam, </em>(<em>2011</em>)Wed Nov 16 16:51:09 CET 2011[Amsterdam]--Formal concept analysis and tag recommendations in collaborative tagging systems2011bibsonomy bookmarking dissertation fca recommender social tag tagging taggingsurvey http://www.bibsonomy.org/bibtex/2b02daac1201473600b7c8d2553865b4a/hothohotho2011-11-16T13:36:54+01:00bookmarking dissertation folksonomy recommender social tagging taggingsurvey <span class="authorEditorList"><a href="/author/Bogers">Toine Bogers</a> </span><em>Tilburg University, </em><em>Tilburg, The Netherlands, </em>(<em>December 2009</em>)Wed Nov 16 13:36:54 CET 2011Tilburg, The NetherlandsdecRecommender Systems for Social Bookmarking2009bookmarking dissertation folksonomy recommender social tagging taggingsurvey 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. http://www.bibsonomy.org/bibtex/28d38bdb12f6f2f89bd3c34d200e48b72/hothohotho2011-06-08T13:48:43+02:00item recommender tag toread user <span class="authorEditorList"><a href="/author/Sen">Shilad Sen</a>, <a href="/author/Vig">Jesse Vig</a>, and <a href="/author/Riedl">John Riedl</a> </span><em>WWW, </em><em>page 671-680. </em><em>ACM, </em>(<em>2009</em>)Wed Jun 08 13:48:43 CEST 2011WWWconf/www/2009671-680Tagommenders: connecting users to items through tags.2009item recommender tag toread user dblphttp://www.bibsonomy.org/bibtex/22b9dd91a3162d821abbe620942772464/hothohotho2011-06-01T08:14:28+02:00factorization model recommendation recommender tagging taggingsurvey <span class="authorEditorList"><a href="/author/Zhou">Tom Chao Zhou</a>, <a href="/author/Ma">Hao Ma</a>, <a href="/author/King">Irwin King</a>, and <a href="/author/Lyu">Michael R. Lyu</a> </span><em>Computational Science and Engineering, IEEE International Conference on</em> (<em>2009</em>)Wed Jun 01 08:14:28 CEST 2011Los Alamitos, CA, USAComputational Science and Engineering, IEEE International Conference on194-199TagRec: Leveraging Tagging Wisdom for Recommendation42009factorization model recommendation recommender tagging taggingsurvey TagRec: Leveraging Tagging Wisdom for Recommendationhttp://www.bibsonomy.org/bibtex/252a9e5fd121bf7be4fa8670cc93a7197/hothohotho2011-05-18T16:19:56+02:00decomposion recommender social tagging taggingsurvey tensor <span class="authorEditorList"><a href="/author/Cai">Yuanzhe Cai</a>, <a href="/author/Zhang">Miao Zhang</a>, <a href="/author/Luo">Dijun Luo</a>, <a href="/author/Ding">Chris Ding</a>, and <a href="/author/Chakravarthy">Sharma Chakravarthy</a> </span><em>Proceedings of the fourth ACM international conference on Web search and data mining, </em><em>page 695--704. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2011</em>)Wed May 18 16:19:56 CEST 2011New York, NY, USAProceedings of the fourth ACM international conference on Web search and data mining695--704WSDM '11Low-order tensor decompositions for social tagging recommendation2011decomposion recommender social tagging taggingsurvey tensor Social tagging recommendation is an urgent and useful enabling technology for Web 2.0. In this paper, we present a systematic study of low-order tensor decomposition approach that are specifically targeted at the very sparse data problem in tagging recommendation problem. Low-order polynomials have low functional complexity, are uniquely capable of enhancing statistics and also avoids over-fitting than traditional tensor decompositions such as Tucker and Parafac decompositions. We perform extensive experiments on several datasets and compared with 6 existing methods. Experimental results demonstrate that our approach outperforms existing approaches.Low-order tensor decompositions for social tagging recommendationhttp://www.bibsonomy.org/bibtex/2331c4767c9abe7fe3e6bb214a49f0cee/folkefolke2011-05-11T14:43:54+02:00lda recommender tag warwick <span class="authorEditorList"><a href="/author/Krestel">Ralf Krestel</a>, <a href="/author/Fankhauser">Peter Fankhauser</a>, and <a href="/author/Nejdl">Wolfgang Nejdl</a> </span><em>Proceedings of the third ACM conference on Recommender systems, </em><em>page 61--68. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Wed May 11 14:43:54 CEST 2011New York, NY, USAProceedings of the third ACM conference on Recommender systems61--68RecSys '09Latent dirichlet allocation for tag recommendation2009lda recommender tag warwick Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse. Especially new resources annotated by only a few users have often rather idiosyncratic tags that do not reflect a common perspective useful for search. In this paper we introduce an approach based on Latent Dirichlet Allocation (LDA) for recommending tags of resources in order to improve search. Resources annotated by many users and thus equipped with a fairly stable and complete tag set are used to elicit latent topics to which new resources with only a few tags are mapped. Based on this, other tags belonging to a topic can be recommended for the new resource. Our evaluation shows that the approach achieves significantly better precision and recall than the use of association rules, suggested in previous work, and also recommends more specific tags. Moreover, extending resources with these recommended tags significantly improves search for new resources.Latent dirichlet allocation for tag recommendationhttp://www.bibsonomy.org/bibtex/280d47c0270df02171e97c9509f87b1b9/folkefolke2011-05-11T14:38:43+02:00folkrank graph recommender tag warwick <span class="authorEditorList"><a href="/author/Ramezani">M. Ramezani</a> </span><em>Journal of Emerging Technologies in Web Intelligence</em> <em>3(2):168--176</em> (<em>2011</em>)Wed May 11 14:38:43 CEST 2011Journal of Emerging Technologies in Web Intelligence2168--176{Improving Graph-based Approaches for Personalized Tag Recommendation}32011folkrank graph recommender tag warwick http://www.bibsonomy.org/bibtex/26e732d28b921777fcd7b982d52e4f131/folkefolke2011-05-11T14:36:28+02:00recommender semantic sna tag warwick web <span class="authorEditorList"><a href="/author/Voon">Wong Pei Voon</a>, <a href="/author/Mustapha">Norwati Mustapha</a>, and <a href="/author/Sulaiman">Md. Nasir Sulaiman</a> </span><em>Proceedings of the 12th International Conference on Information Integration and Web-based Applications \&amp;\#38; Services, </em><em>page 909--912. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Wed May 11 14:36:28 CEST 2011New York, NY, USAProceedings of the 12th International Conference on Information Integration and Web-based Applications \&\#38; Services909--912iiWAS '10A tag recommendation approach based on social semantic web2010recommender semantic sna tag warwick web Collaborative tagging applications or folksonomies allow internet users to annotate resources with personalized tags. However, freedom afforded users come at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. Ambiguity can mislead users as they search for relevant resources. Recommenders that avoid ambiguous tags may be penalized by standard utility metrics for not promoting such tags.</p> <p>To provide effectiveness of tag recommendation in term of accuracy and coverage, we propose a new method for tag recommendation approach based on social semantic web to overcome ambiguity and presenting users tags and resources that correspond more closely. This method is a combination of semantic content analysis methods and social network analysis methods.A tag recommendation approach based on social semantic webhttp://www.bibsonomy.org/bibtex/2f66b82fc919462c25698392c3cf4e6fa/folkefolke2011-05-11T14:32:17+02:00based context folkrank graph group recommender warwick <span class="authorEditorList"><a href="/author/Abel">Fabian Abel</a>, <a href="/author/Henze">Nicola Henze</a>, and <a href="/author/Krause">Daniel Krause</a> </span><em>Proceeding of the 17th ACM conference on Information and knowledge management, </em><em>page 1429--1430. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed May 11 14:32:17 CEST 2011New York, NY, USAProceeding of the 17th ACM conference on Information and knowledge management1429--1430CIKM '08Ranking in folksonomy systems: can context help?2008based context folkrank graph group recommender warwick Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.Ranking in folksonomy systemshttp://www.bibsonomy.org/bibtex/21a8829cde1cb26241a48901e28a953d2/dbenzdbenz2011-02-17T17:42:18+01:00bibster change_operation ol_web2.0 ontologies ontology personalized recommender widely_related <span class="authorEditorList"><a href="/author/Haase">Peter Haase</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmidt-Thieme">Lars Schmidt-Thieme</a>, and <a href="/author/Sure">York Sure</a> </span><em>ESWC, </em><em>volume 3532 of Lecture Notes in Computer Science, </em><em>page 486-499. </em><em>Springer, </em>(<em>2005</em>)Thu Feb 17 17:42:18 CET 2011ESWCconf/esws/2005486-499Lecture Notes in Computer ScienceCollaborative and Usage-Driven Evolution of Personal Ontologies.35322005bibster change_operation ol_web2.0 ontologies ontology personalized recommender widely_related 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.dblphttp://www.bibsonomy.org/bibtex/249600df05a884106989d71dedcaa7e1b/hothohotho2011-02-09T15:32:02+01:00evaluation metrics recommender survey toread <span class="authorEditorList"><a href="/author/Gunawardana">Asela Gunawardana</a>, and <a href="/author/Shani">Guy Shani</a> </span><em>volume v10 of 2935, </em>(<em>2009</em>)Wed Feb 09 15:32:02 CET 20112935 A Survey of Accuracy Evaluation Metrics of Recommendation Tasks v102009evaluation metrics recommender survey toread http://www.bibsonomy.org/bibtex/22c8764f2fe11ef1ae43fc0a5b51301ae/hothohotho2011-01-31T20:44:57+01:00collaborative recommender social tagging taggingsurvey toread <span class="authorEditorList"><a href="/author/Santander">Denis P. Santander</a>, and <a href="/author/Brusilovsky">Peter Brusilovsky</a> </span><em>Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on</em> (<em>2010</em>)Mon Jan 31 20:44:57 CET 2011Los Alamitos, CA, USAWeb Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on136--142{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}12010collaborative recommender social tagging taggingsurvey toread {Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}CiteULike: Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles