PUMA publications for /user/hotho/recommenderhttps://puma.uni-kassel.de/user/hotho/recommenderPUMA RSS feed for /user/hotho/recommender2024-03-29T11:42:27+01:00Recommendation in the Social Webhttps://puma.uni-kassel.de/bibtex/241dbb2c9f71440c9aa402f8966117979/hothohotho2011-11-30T14:46:32+01:002011 collaborative myown recommender social tagging taggingsurvey web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robin Burke" itemprop="url" href="/author/Robin%20Burke"><span itemprop="name">R. Burke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jonathan Gemmell" itemprop="url" href="/author/Jonathan%20Gemmell"><span itemprop="name">J. Gemmell</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>AI Magazine</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">32 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">46--56</span></em> </span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></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.Challenges in Tag Recommendations for Collaborative Tagging Systemshttps://puma.uni-kassel.de/bibtex/27d41d332cccc3e7ba8e7dadfb7996337/hothohotho2012-02-09T09:26:57+01:002012 bookmarking challenge collaborative dc09 discovery folksonomy myown recommender rsdc08 social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Recommender Systems for the Social Web</span>, </em><em>Volume 32 von Intelligent Systems Reference Library, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></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.Recommender Systems for Social Tagging Systemshttps://puma.uni-kassel.de/bibtex/287d6883ebd98e8810be45d7e7e4ade96/hothohotho2012-02-14T08:29:50+01:002012 bookmarking collaborative folksonomy myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Balby Marinho" itemprop="url" href="/author/L.%20Balby%20Marinho"><span itemprop="name">L. Balby Marinho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Jäschke" itemprop="url" href="/author/R.%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Nanopoulos" itemprop="url" href="/author/A.%20Nanopoulos"><span itemprop="name">A. Nanopoulos</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Rendle" itemprop="url" href="/author/S.%20Rendle"><span itemprop="name">S. Rendle</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Schmidt-Thieme" itemprop="url" href="/author/L.%20Schmidt-Thieme"><span itemprop="name">L. Schmidt-Thieme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Symeonidis" itemprop="url" href="/author/P.%20Symeonidis"><span itemprop="name">P. Symeonidis</span></a></span>. </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Februar 2012<meta content="Februar 2012" itemprop="datePublished"/></span></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.I tag, you tag: translating tags for advanced user modelshttps://puma.uni-kassel.de/bibtex/2224e7bdc753e1823fc17828f2c760b6e/hothohotho2012-02-22T08:48:28+01:00folksonomy model recommender tagging taggingsurvey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Wetzker" itemprop="url" href="/author/Robert%20Wetzker"><span itemprop="name">R. Wetzker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Carsten Zimmermann" itemprop="url" href="/author/Carsten%20Zimmermann"><span itemprop="name">C. Zimmermann</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christian Bauckhage" itemprop="url" href="/author/Christian%20Bauckhage"><span itemprop="name">C. Bauckhage</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sahin Albayrak" itemprop="url" href="/author/Sahin%20Albayrak"><span itemprop="name">S. Albayrak</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the third ACM international conference on Web search and data mining</span>, </em></span><em>Seite <span itemprop="pagination">71--80</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Wed Feb 22 08:48:28 CET 2012New York, NY, USAProceedings of the third ACM international conference on Web search and data mining71--80WSDM '10I tag, you tag: translating tags for advanced user models2010folksonomy model recommender tagging taggingsurvey Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios.I tag, you tag3rd workshop on recommender systems and the social webhttps://puma.uni-kassel.de/bibtex/211fe6dd3da33f0aa0a40c998e5193ab8/hothohotho2012-04-03T15:15:52+02:002011 cochair myown recommender social systems workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jill Freyne" itemprop="url" href="/author/Jill%20Freyne"><span itemprop="name">J. Freyne</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ido Guy" itemprop="url" href="/author/Ido%20Guy"><span itemprop="name">I. Guy</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the fifth ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">383--384</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Tue Apr 03 15:15:52 CEST 2012New York, NY, USAProceedings of the fifth ACM conference on Recommender systems383--384RecSys '113rd workshop on recommender systems and the social web20112011 cochair myown recommender social systems workshop The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm3rd workshop on recommender systems and the social webLatent Collaborative Retrievalhttps://puma.uni-kassel.de/bibtex/279c6771a9b032497635d5f39a39e921a/hothohotho2012-08-15T11:07:09+02:00recommender tensor toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jason Weston" itemprop="url" href="/author/Jason%20Weston"><span itemprop="name">J. Weston</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chong Wang" itemprop="url" href="/author/Chong%20Wang"><span itemprop="name">C. Wang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ron Weiss" itemprop="url" href="/author/Ron%20Weiss"><span itemprop="name">R. Weiss</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adam Berenzweig" itemprop="url" href="/author/Adam%20Berenzweig"><span itemprop="name">A. Berenzweig</span></a></span>. </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)<em>cite arxiv:1206.4603Comment: ICML2012.</em>Wed Aug 15 11:07:09 CEST 2012cite arxiv:1206.4603Comment: ICML2012Latent Collaborative Retrieval2012recommender tensor toread Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.Latent Collaborative RetrievalRecommender Systems Handbookhttps://puma.uni-kassel.de/bibtex/249d1b9115124c64075d6c2c559504b22/hothohotho2012-09-03T17:58:16+02:00***** handbook overview recommender survey systems <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Francesco Ricci" itemprop="url" href="/author/Francesco%20Ricci"><span itemprop="name">F. Ricci</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Lior Rokach" itemprop="url" href="/author/Lior%20Rokach"><span itemprop="name">L. Rokach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Bracha Shapira" itemprop="url" href="/author/Bracha%20Shapira"><span itemprop="name">B. Shapira</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Paul B. Kantor" itemprop="url" href="/author/Paul%20B.%20Kantor"><span itemprop="name">P. Kantor</span></a></span> (Hrsg.).
. </span><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Mon Sep 03 17:58:16 CEST 2012Recommender Systems HandbookRecommender Systems Handbook2011***** handbook overview recommender survey systems RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social webhttps://puma.uni-kassel.de/bibtex/2354947709c23c90b18dae862c46b2761/hothohotho2012-09-15T12:32:42+02:002012 acm myown recommender rsweb social web workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dietmar Jannach" itemprop="url" href="/author/Dietmar%20Jannach"><span itemprop="name">D. Jannach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Werner Geyer" itemprop="url" href="/author/Werner%20Geyer"><span itemprop="name">W. Geyer</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)<em>609126.</em>Sat Sep 15 12:32:42 CEST 2012New York, NY, USA609126RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web20122012 acm myown recommender rsweb social web workshop The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social webLeveraging publication metadata and social data into FolkRank for scientific publication recommendationhttps://puma.uni-kassel.de/bibtex/2e5c2266da34a9167352615827cc4670d/hothohotho2012-09-15T12:37:17+02:002012 bookmarking folkrank myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">9--16</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Sat Sep 15 12:37:17 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web9--16RSWeb '12Leveraging publication metadata and social data into FolkRank for scientific publication recommendation20122012 bookmarking folkrank myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.Leveraging publication metadata and social data into FolkRank for scientific publication recommendationRecommender systemshttps://puma.uni-kassel.de/bibtex/29594d6b87d49d22b783b9c95da1f59af/hothohotho2012-10-12T12:09:48+02:00recommender survey toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Linyuan Lü" itemprop="url" href="/author/Linyuan%20L%c3%bc"><span itemprop="name">L. Lü</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Matúš Medo" itemprop="url" href="/author/Mat%c3%ba%c5%a1%20Medo"><span itemprop="name">M. Medo</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Chi Ho Yeung" itemprop="url" href="/author/Chi%20Ho%20Yeung"><span itemprop="name">C. Yeung</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yi-Cheng Zhang" itemprop="url" href="/author/Yi-Cheng%20Zhang"><span itemprop="name">Y. Zhang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Zi-Ke Zhang" itemprop="url" href="/author/Zi-Ke%20Zhang"><span itemprop="name">Z. Zhang</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tao Zhou" itemprop="url" href="/author/Tao%20Zhou"><span itemprop="name">T. Zhou</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Physics Reports</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">519 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">1 - 49</span></em> </span>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)<em>Recommender Systems.</em>Fri Oct 12 12:09:48 CEST 2012Physics ReportsRecommender Systems11 - 49Recommender systems5192012recommender survey toread The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.ScienceDirect.com - Physics Reports - Recommender systemsThe challenge of recommender systems challenges.https://puma.uni-kassel.de/bibtex/2e030cb4883f2e989a903ed65d0a13308/hothohotho2012-12-11T09:33:39+01:002012 myown recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alan Said" itemprop="url" href="/author/Alan%20Said"><span itemprop="name">A. Said</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Domonkos Tikk" itemprop="url" href="/author/Domonkos%20Tikk"><span itemprop="name">D. Tikk</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">RecSys</span>, </em></span><em>Seite <span itemprop="pagination">9-10</span>. </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Tue Dec 11 09:33:39 CET 2012RecSysconf/recsys/20129-10The challenge of recommender systems challenges.20122012 myown recommender 4th ACM RecSys workshop on recommender systems and the social web.https://puma.uni-kassel.de/bibtex/2fe34d757dd2ec79a40d2baa55115898d/hothohotho2012-12-11T09:36:37+01:002012 myown recommender workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dietmar Jannach" itemprop="url" href="/author/Dietmar%20Jannach"><span itemprop="name">D. Jannach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Werner Geyer" itemprop="url" href="/author/Werner%20Geyer"><span itemprop="name">W. Geyer</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">RecSys</span>, </em></span><em>Seite <span itemprop="pagination">345-346</span>. </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Tue Dec 11 09:36:37 CET 2012RecSysconf/recsys/2012345-3464th ACM RecSys workshop on recommender systems and the social web.20122012 myown recommender workshop Recommending Given Nameshttps://puma.uni-kassel.de/bibtex/241f92650f0f7d78366febc1832cedba9/hothohotho2013-02-19T10:17:06+01:00nameling recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Folke Mitzlaff" itemprop="url" href="/author/Folke%20Mitzlaff"><span itemprop="name">F. Mitzlaff</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)<em>cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013.</em>Tue Feb 19 10:17:06 CET 2013cite arxiv:1302.4412Comment: Baseline results for the ECML PKDD Discovery Challenge 2013Recommending Given Names2013nameling recommender All over the world, future parents are facing the task of finding a suitable given name for their child. This choice is influenced by different factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and application of interrelations among given names from a data mining perspective. The present work tackles the problem of recommending given names, by firstly mining for inter-name relatedness in data from the Social Web. Based on these results, the name search engine "Nameling" was built, which attracted more than 35,000 users within less than six months, underpinning the relevance of the underlying recommendation task. The accruing usage data is then used for evaluating different state-of-the-art recommendation systems, as well our new \NR algorithm which we adopted from our previous work on folksonomies and which yields the best results, considering the trade-off between prediction accuracy and runtime performance as well as its ability to generate personalized recommendations. We also show, how the gathered inter-name relationships can be used for meaningful result diversification of PageRank-based recommendation systems. As all of the considered usage data is made publicly available, the present work establishes baseline results, encouraging other researchers to implement advanced recommendation systems for given names.Recommending Given NamesDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/hothohotho2013-10-10T12:27:01+02:002013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Thu Oct 10 12:27:01 CEST 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.Tag Recommendations for SensorFolkSonomieshttps://puma.uni-kassel.de/bibtex/22bab3f013052bc741e795c5c61aea5c9/hothohotho2014-01-08T19:55:15+01:002013 everyaware folksonomy myown recommender sensor tag <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Juergen Mueller" itemprop="url" href="/author/Juergen%20Mueller"><span itemprop="name">J. Mueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Becker" itemprop="url" href="/author/Martin%20Becker"><span itemprop="name">M. Becker</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings</span>, </em></span><em> 1066, </em><em>Aachen, Germany, </em><em><span itemprop="publisher">CEUR-WS</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Wed Jan 08 19:55:15 CET 2014Aachen, GermanyRecommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. ProceedingsTag Recommendations for SensorFolkSonomies106620132013 everyaware folksonomy myown recommender sensor tag With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case.Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.https://puma.uni-kassel.de/bibtex/2aca768068f09003e97b51d48ec092ddc/hothohotho2014-01-20T14:27:08+01:002013 bibsonomy l3s myown recommender social web workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Dietmar Jannach" itemprop="url" href="/author/Dietmar%20Jannach"><span itemprop="name">D. Jannach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Werner Geyer" itemprop="url" href="/author/Werner%20Geyer"><span itemprop="name">W. Geyer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Jill Freyne" itemprop="url" href="/author/Jill%20Freyne"><span itemprop="name">J. Freyne</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Ido Guy" itemprop="url" href="/author/Ido%20Guy"><span itemprop="name">I. Guy</span></a></span> (Hrsg.).
. </span><em>Volume 1066 von CEUR Workshop Proceedings, </em><em><span itemprop="publisher">CEUR-WS.org</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Jan 20 14:27:08 CET 2014RSWeb@RecSysCEUR Workshop ProceedingsProceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.106620132013 bibsonomy l3s myown recommender social web workshop LA-LDA: A Limited Attention Topic Model for Social Recommendationhttps://puma.uni-kassel.de/bibtex/284ae222ddb615ca8ae9421a29c07a8f6/hothohotho2014-02-27T11:05:27+01:00lda model recommender sota topic <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jeon-Hyung Kang" itemprop="url" href="/author/Jeon-Hyung%20Kang"><span itemprop="name">J. Kang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kristina Lerman" itemprop="url" href="/author/Kristina%20Lerman"><span itemprop="name">K. Lerman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lise Getoor" itemprop="url" href="/author/Lise%20Getoor"><span itemprop="name">L. Getoor</span></a></span>. </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)<em>cite arxiv:1301.6277Comment: The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013).</em>Thu Feb 27 11:05:27 CET 2014cite arxiv:1301.6277Comment: The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP 2013)LA-LDA: A Limited Attention Topic Model for Social Recommendation2013lda model recommender sota topic Social media users have finite attention which limits the number of incoming
messages from friends they can process. Moreover, they pay more attention to
opinions and recommendations of some friends more than others. In this paper,
we propose LA-LDA, a latent topic model which incorporates limited,
non-uniformly divided attention in the diffusion process by which opinions and
information spread on the social network. We show that our proposed model is
able to learn more accurate user models from users' social network and item
adoption behavior than models which do not take limited attention into account.
We analyze voting on news items on the social news aggregator Digg and show
that our proposed model is better able to predict held out votes than
alternative models. Our study demonstrates that psycho-socially motivated
models have better ability to describe and predict observed behavior than
models which only consider topics.LA-LDA: A Limited Attention Topic Model for Social RecommendationThe sixth ACM RecSys workshop on recommender systems and the social
webhttps://puma.uni-kassel.de/bibtex/25773f799bec72240eda5e6cfb6a03d7b/hothohotho2014-10-29T11:28:04+01:002014 introduction myown recommender social workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dietmar Jannach" itemprop="url" href="/author/Dietmar%20Jannach"><span itemprop="name">D. Jannach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jill Freyne" itemprop="url" href="/author/Jill%20Freyne"><span itemprop="name">J. Freyne</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Werner Geyer" itemprop="url" href="/author/Werner%20Geyer"><span itemprop="name">W. Geyer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ido Guy" itemprop="url" href="/author/Ido%20Guy"><span itemprop="name">I. Guy</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014</span>, </em></span><em>Seite <span itemprop="pagination">395</span>. </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Wed Oct 29 11:28:04 CET 2014Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014395The sixth {ACM} RecSys workshop on recommender systems and the social
web20142014 introduction myown recommender social workshop Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014https://puma.uni-kassel.de/bibtex/209cb7c63e60bd3c5e6773c9c871a8aba/hothohotho2014-10-29T11:31:47+01:002014 myown proceedings recommender social workshop <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Dietmar Jannach" itemprop="url" href="/author/Dietmar%20Jannach"><span itemprop="name">D. Jannach</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Jill Freyne" itemprop="url" href="/author/Jill%20Freyne"><span itemprop="name">J. Freyne</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Werner Geyer" itemprop="url" href="/author/Werner%20Geyer"><span itemprop="name">W. Geyer</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Ido Guy" itemprop="url" href="/author/Ido%20Guy"><span itemprop="name">I. Guy</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Bamshad Mobasher" itemprop="url" href="/author/Bamshad%20Mobasher"><span itemprop="name">B. Mobasher</span></a></span> (Hrsg.).
. </span><em>Volume 1271 von CEUR Workshop Proceedings, </em><em><span itemprop="publisher">CEUR-WS.org</span>, </em>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Wed Oct 29 11:31:47 CET 2014{CEUR} Workshop ProceedingsProceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014127120142014 myown proceedings recommender social workshop Content-boosted Collaborative Filtering for Improved Recommendationshttps://puma.uni-kassel.de/bibtex/2a4917f0299f48e403966a8003ebd50be/hothohotho2015-02-16T17:25:55+01:00collaborative content filtering hybrid recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Prem Melville" itemprop="url" href="/author/Prem%20Melville"><span itemprop="name">P. Melville</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raymod J. Mooney" itemprop="url" href="/author/Raymod%20J.%20Mooney"><span itemprop="name">R. Mooney</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ramadass Nagarajan" itemprop="url" href="/author/Ramadass%20Nagarajan"><span itemprop="name">R. Nagarajan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Eighteenth National Conference on Artificial Intelligence</span>, </em></span><em>Seite <span itemprop="pagination">187--192</span>. </em><em>Menlo Park, CA, USA, </em><em><span itemprop="publisher">American Association for Artificial Intelligence</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Mon Feb 16 17:25:55 CET 2015Menlo Park, CA, USAEighteenth National Conference on Artificial Intelligence187--192Content-boosted Collaborative Filtering for Improved Recommendations2002collaborative content filtering hybrid recommender Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.