PUMA publications for /user/jaeschke/recommenderhttps://puma.uni-kassel.de/user/jaeschke/recommenderPUMA RSS feed for /user/jaeschke/recommender2024-03-29T00:06:26+01:00Item-based collaborative filtering recommendation algorithmshttps://puma.uni-kassel.de/bibtex/216f38785d7829500ed41c610a5eff9a2/jaeschkejaeschke2013-04-16T07:46:25+02:00cf collaborative filtering item recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Badrul Sarwar" itemprop="url" href="/author/Badrul%20Sarwar"><span itemprop="name">B. Sarwar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Karypis" itemprop="url" href="/author/George%20Karypis"><span itemprop="name">G. Karypis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joseph Konstan" itemprop="url" href="/author/Joseph%20Konstan"><span itemprop="name">J. Konstan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Riedl" itemprop="url" href="/author/John%20Riedl"><span itemprop="name">J. Riedl</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 10th international conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">285--295</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Tue Apr 16 07:46:25 CEST 2013New York, NY, USAProceedings of the 10th international conference on World Wide Web285--295Item-based collaborative filtering recommendation algorithms2001cf collaborative filtering item recommender Building an Environmental Information System for Personalized Content Deliveryhttps://puma.uni-kassel.de/bibtex/2ad68ea956a3bf495d64194ffce367a20/jaeschkejaeschke2013-01-14T08:53:07+01:00environment personalization recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leo Wanner" itemprop="url" href="/author/Leo%20Wanner"><span itemprop="name">L. Wanner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefanos Vrochidis" itemprop="url" href="/author/Stefanos%20Vrochidis"><span itemprop="name">S. Vrochidis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sara Tonelli" itemprop="url" href="/author/Sara%20Tonelli"><span itemprop="name">S. Tonelli</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jürgen Moßgraber" itemprop="url" href="/author/J%c3%bcrgen%20Mo%c3%9fgraber"><span itemprop="name">J. Moßgraber</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Harald Bosch" itemprop="url" href="/author/Harald%20Bosch"><span itemprop="name">H. Bosch</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ari Karppinen" itemprop="url" href="/author/Ari%20Karppinen"><span itemprop="name">A. Karppinen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maria Myllynen" itemprop="url" href="/author/Maria%20Myllynen"><span itemprop="name">M. Myllynen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marco Rospocher" itemprop="url" href="/author/Marco%20Rospocher"><span itemprop="name">M. Rospocher</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nadjet Bouayad-Agha" itemprop="url" href="/author/Nadjet%20Bouayad-Agha"><span itemprop="name">N. Bouayad-Agha</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ulrich Bügel" itemprop="url" href="/author/Ulrich%20B%c3%bcgel"><span itemprop="name">U. Bügel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerard Casamayor" itemprop="url" href="/author/Gerard%20Casamayor"><span itemprop="name">G. Casamayor</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Ertl" itemprop="url" href="/author/Thomas%20Ertl"><span itemprop="name">T. Ertl</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ioannis Kompatsiaris" itemprop="url" href="/author/Ioannis%20Kompatsiaris"><span itemprop="name">I. Kompatsiaris</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tarja Koskentalo" itemprop="url" href="/author/Tarja%20Koskentalo"><span itemprop="name">T. Koskentalo</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Simon Mille" itemprop="url" href="/author/Simon%20Mille"><span itemprop="name">S. Mille</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anastasia Moumtzidou" itemprop="url" href="/author/Anastasia%20Moumtzidou"><span itemprop="name">A. Moumtzidou</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Emanuele Pianta" itemprop="url" href="/author/Emanuele%20Pianta"><span itemprop="name">E. Pianta</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Horacio Saggion" itemprop="url" href="/author/Horacio%20Saggion"><span itemprop="name">H. Saggion</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Luciano Serafini" itemprop="url" href="/author/Luciano%20Serafini"><span itemprop="name">L. Serafini</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Virpi Tarvainen" itemprop="url" href="/author/Virpi%20Tarvainen"><span itemprop="name">V. Tarvainen</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Environmental Software Systems. Frameworks of eEnvironment</span>, </em><em>Volume 359 von IFIP Advances in Information and Communication Technology, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Mon Jan 14 08:53:07 CET 2013Berlin/HeidelbergEnvironmental Software Systems. Frameworks of eEnvironment169--176IFIP Advances in Information and Communication TechnologyBuilding an Environmental Information System for Personalized Content Delivery3592011environment personalization recommender stair Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.Recommender Systems for the Social Webhttps://puma.uni-kassel.de/bibtex/2aceda1417241eb872dc27db7c7e4158a/jaeschkejaeschke2012-12-14T10:43:16+01:00recommender social stair system web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="José J. and Pazos Arias" itemprop="url" href="/author/Jos%c3%a9%20J.%20and%20Pazos%20Arias"><span itemprop="name">J. and Pazos Arias</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Ana Fernández Vilas" itemprop="url" href="/author/Ana%20Fern%c3%a1ndez%20Vilas"><span itemprop="name">A. Fernández Vilas</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Rebeca P. Díaz Redondo" itemprop="url" href="/author/Rebeca%20P.%20D%c3%adaz%20Redondo"><span itemprop="name">R. Díaz Redondo</span></a></span> (Hrsg.).
. </span><em>Intelligent Systems Reference Library </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em>(<em><span>2012<meta content="2012" itemprop="datePublished"/></span></em>)Fri Dec 14 10:43:16 CET 2012Berlin/HeidelbergIntelligent Systems Reference LibraryRecommender Systems for the Social Web322012recommender social stair system web Recommender Systems Handbookhttps://puma.uni-kassel.de/bibtex/278d23da5e3ac4f79ba59f94ecf434cf6/jaeschkejaeschke2012-12-14T09:50:58+01:00handbook recommender stair system <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 US</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Fri Dec 14 09:50:58 CET 2012Recommender Systems Handbook2011handbook recommender stair system Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. Recommender systems have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed and during the last decade, many of them have also been successfully deployed in commercial environments. Development of recommender systems is a multi-disciplinary effort which involves experts from various fields such as Artificial intelligence, Human Computer Interaction, Information Technology, Data Mining, Statistics, Adaptive User Interfaces, Decision Support Systems, Marketing, or Consumer Behavior. Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. This is the first comprehensive book which is dedicated entirely to the field of recommender systems and covers several aspects of the major techniques. Its informative, factual pages will provide researchers, students and practitioners in industry with a comprehensive, yet concise and convenient reference source to recommender systems. The book describes in detail the classical methods, as well as extensions and novel approaches that were recently introduced. The book consists of five parts: techniques, applications and evaluation of recommender systems, interacting with recommender systems, recommender systems and communities, and advanced algorithms. The first part presents the most popular and fundamental techniques used nowadays for building recommender systems, such as collaborative filtering, content-based filtering, data mining methods and context-aware methods. The second part starts by surveying techniques and approaches that have been used to evaluate the quality of the recommendations. Then deals with the practical aspects of designing recommender systems, it describes design and implementation consideration, setting guidelines for the selection of the more suitable algorithms. The section continues considering aspects that may affect the design and finally, it discusses methods, challenges and measures to be applied for the evaluation of the developed systems. The third part includes papers dealing with a number of issues related to the presentation, browsing, explanation and visualization of the recommendations, and techniques that make the recommendation process more structured and conversational. The fourth part is fully dedicated to a rather new topic, which is however rooted in the core idea of a collaborative recommender, i.e., exploiting user generated content of various types to build new types and more credible recommendations. Finally the last section collects a few papers on some advanced topics, such as the exploitation of active learning principles to guide the acquisition of new knowledge, techniques suitable for making a recommender system robust against attacks of malicious users, and recommender systems that aggregate multiple types of user feedbacks and preferences to build more reliable recommendations. We would like to thank all authors for their valuable contributions. We would like to express gratitude for all reviewers that generously gave comments on drafts or counsel otherwise.We would like to express our special thanks to Susan Lagerstrom-Fife and staff members of Springer for their kind cooperation throughout the production of this book. Finally, we wish this handbook will contribute to the growth of this subject, we wish to the novices a fruitful learning path, and to those more experts a compelling application of the ideas discussed in this handbook and a fruitful development of this challenging research area. Improving recommender systems with adaptive conversational strategieshttps://puma.uni-kassel.de/bibtex/209f59f9da4949dd68dc0c7c3f8fb3e5b/jaeschkejaeschke2012-12-13T10:40:34+01:00adaptivity conversational interactive recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tariq Mahmood" itemprop="url" href="/author/Tariq%20Mahmood"><span itemprop="name">T. Mahmood</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Francesco Ricci" itemprop="url" href="/author/Francesco%20Ricci"><span itemprop="name">F. Ricci</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 20th ACM conference on Hypertext and hypermedia</span>, </em></span><em>Seite <span itemprop="pagination">73--82</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Thu Dec 13 10:40:34 CET 2012New York, NY, USAProceedings of the 20th ACM conference on Hypertext and hypermedia73--82Improving recommender systems with adaptive conversational strategies2009adaptivity conversational interactive recommender stair Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.Interaction design guidelines on critiquing-based recommender systemshttps://puma.uni-kassel.de/bibtex/2f0e063a97473519ca650fe029da73ce7/jaeschkejaeschke2012-12-13T10:31:05+01:00critiquing interaction recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Li Chen" itemprop="url" href="/author/Li%20Chen"><span itemprop="name">L. Chen</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pearl Pu" itemprop="url" href="/author/Pearl%20Pu"><span itemprop="name">P. Pu</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>User Modeling and User-Adapted Interaction</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">19 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">167--206</span></em> </span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Thu Dec 13 10:31:05 CET 2012User Modeling and User-Adapted Interaction3167--206Interaction design guidelines on critiquing-based recommender systems192009critiquing interaction recommender stair A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user’s preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user’s interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: A personalized system for conversational recommendationshttps://puma.uni-kassel.de/bibtex/2ea5a393bf4ccba3dd4e07b348199c202/jaeschkejaeschke2012-12-13T10:16:16+01:00conversational personalization recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Cynthia A. Thompson" itemprop="url" href="/author/Cynthia%20A.%20Thompson"><span itemprop="name">C. Thompson</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mehmet H. Göker" itemprop="url" href="/author/Mehmet%20H.%20G%c3%b6ker"><span itemprop="name">M. Göker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pat Langley" itemprop="url" href="/author/Pat%20Langley"><span itemprop="name">P. Langley</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>Journal of Artificial Intelligence Research</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">21 </span></span>(<span itemprop="issueNumber">1</span>):
<span itemprop="pagination">393--428</span></em> </span>(<em><span>März 2004<meta content="März 2004" itemprop="datePublished"/></span></em>)Thu Dec 13 10:16:16 CET 2012Journal of Artificial Intelligence Researchmar1393--428A personalized system for conversational recommendations212004conversational personalization recommender stair Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.A Taxonomy of Recommender Agents on the Internethttps://puma.uni-kassel.de/bibtex/2f713e3f6acc112d9fbfd10216589d7db/jaeschkejaeschke2012-12-10T09:57:34+01:00agent recommender survey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miquel Montaner" itemprop="url" href="/author/Miquel%20Montaner"><span itemprop="name">M. Montaner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Beatriz López" itemprop="url" href="/author/Beatriz%20L%c3%b3pez"><span itemprop="name">B. López</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Josep Lluís de la Rosa" itemprop="url" href="/author/Josep%20Llu%c3%ads%20de%20la%20Rosa"><span itemprop="name">J. de la Rosa</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>Artificial Intelligence Review</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">19 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">285--330</span></em> </span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Mon Dec 10 09:57:34 CET 2012Artificial Intelligence Review4285--330A Taxonomy of Recommender Agents on the Internet192003agent recommender survey Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.Active Learning in Recommender Systemshttps://puma.uni-kassel.de/bibtex/2e0b5682c1c228037aee63a459e2e2c62/jaeschkejaeschke2012-12-10T09:50:46+01:00active learning machine recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Neil Rubens" itemprop="url" href="/author/Neil%20Rubens"><span itemprop="name">N. Rubens</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dain Kaplan" itemprop="url" href="/author/Dain%20Kaplan"><span itemprop="name">D. Kaplan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Masashi Sugiyama" itemprop="url" href="/author/Masashi%20Sugiyama"><span itemprop="name">M. Sugiyama</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Recommender Systems Handbook</span>, </em><em>Kapitel 23, </em><em><span itemprop="publisher">Springer US</span>, </em></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Mon Dec 10 09:50:46 CET 2012Recommender Systems Handbook23735--767Active Learning in Recommender Systems2011active learning machine recommender Matrix Factorization Techniques for Recommender Systemshttps://puma.uni-kassel.de/bibtex/259ab9b2678949949c04b0fe2a431585a/jaeschkejaeschke2012-12-10T09:29:10+01:00factorization matrix recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Y. Koren" itemprop="url" href="/author/Y.%20Koren"><span itemprop="name">Y. Koren</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Bell" itemprop="url" href="/author/R.%20Bell"><span itemprop="name">R. Bell</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C. Volinsky" itemprop="url" href="/author/C.%20Volinsky"><span itemprop="name">C. Volinsky</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>Computer</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">42 </span></span>(<span itemprop="issueNumber">8</span>):
<span itemprop="pagination">30--37</span></em> </span>(<em><span>August 2009<meta content="August 2009" itemprop="datePublished"/></span></em>)Mon Dec 10 09:29:10 CET 2012Computeraug830--37Matrix Factorization Techniques for Recommender Systems422009factorization matrix recommender stair As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.Design and evaluation of a command recommendation system for software applicationshttps://puma.uni-kassel.de/bibtex/23aec947c38d63f96a4242c95a8c85ee7/jaeschkejaeschke2012-12-07T11:07:10+01:00autocad command recommender software stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wei Li" itemprop="url" href="/author/Wei%20Li"><span itemprop="name">W. Li</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Justin Matejka" itemprop="url" href="/author/Justin%20Matejka"><span itemprop="name">J. Matejka</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tovi Grossman" itemprop="url" href="/author/Tovi%20Grossman"><span itemprop="name">T. Grossman</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joseph A. Konstan" itemprop="url" href="/author/Joseph%20A.%20Konstan"><span itemprop="name">J. Konstan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Fitzmaurice" itemprop="url" href="/author/George%20Fitzmaurice"><span itemprop="name">G. Fitzmaurice</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>ACM Transactions on Computer-Human Interaction</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">18 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">6:1--6:35</span></em> </span>(<em><span>Juli 2011<meta content="Juli 2011" itemprop="datePublished"/></span></em>)Fri Dec 07 11:07:10 CET 2012New York, NY, USAACM Transactions on Computer-Human Interactionjul26:1--6:35Design and evaluation of a command recommendation system for software applications182011autocad command recommender software stair We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. A user study showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.Probabilistic models for unified collaborative and content-based recommendation in sparse-data environmentshttps://puma.uni-kassel.de/bibtex/2407a9c070710c3c3b7fe307d384aed37/jaeschkejaeschke2012-12-06T17:23:37+01:00collaborative content probabilistic recommender sparse <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexandrin Popescul" itemprop="url" href="/author/Alexandrin%20Popescul"><span itemprop="name">A. Popescul</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David M. Pennock" itemprop="url" href="/author/David%20M.%20Pennock"><span itemprop="name">D. Pennock</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Lawrence" itemprop="url" href="/author/Steve%20Lawrence"><span itemprop="name">S. Lawrence</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence</span>, </em></span><em>Seite <span itemprop="pagination">437--444</span>. </em><em>San Francisco, CA, USA, </em><em><span itemprop="publisher">Morgan Kaufmann Publishers Inc.</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Thu Dec 06 17:23:37 CET 2012San Francisco, CA, USAProceedings of the Seventeenth conference on Uncertainty in artificial intelligence437--444Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments2001collaborative content probabilistic recommender sparse Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm's (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global probabilistic models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than <i>k</i>-nearest neighbors (<i>k</i>-NN). Global probabilistic models also allow more general inferences than local methods like <i>k</i>-NN.Content-Based Recommendation Systemshttps://puma.uni-kassel.de/bibtex/278e31f13da63399178ec232e4f2f341d/jaeschkejaeschke2012-12-06T10:29:44+01:00content recommender stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael J. Pazzani" itemprop="url" href="/author/Michael%20J.%20Pazzani"><span itemprop="name">M. Pazzani</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Daniel Billsus" itemprop="url" href="/author/Daniel%20Billsus"><span itemprop="name">D. Billsus</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Adaptive Web</span>, </em><em>Volume 4321 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Dec 06 10:29:44 CET 2012Berlin/HeidelbergThe Adaptive Web325--341Lecture Notes in Computer ScienceContent-Based Recommendation Systems43212007content recommender stair This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.Situational reasoning for task-oriented mobile service recommendationhttps://puma.uni-kassel.de/bibtex/235ebbce0abbe9bbef462e5479cb419ed/jaeschkejaeschke2012-12-05T18:02:57+01:00complex knowledge mobile recommender service situation task <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marko Luther" itemprop="url" href="/author/Marko%20Luther"><span itemprop="name">M. Luther</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yusuke Fukazawa" itemprop="url" href="/author/Yusuke%20Fukazawa"><span itemprop="name">Y. Fukazawa</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Matthias Wagner" itemprop="url" href="/author/Matthias%20Wagner"><span itemprop="name">M. Wagner</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shoji Kurakake" itemprop="url" href="/author/Shoji%20Kurakake"><span itemprop="name">S. Kurakake</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>The Knowledge Engineering Review</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">23 </span></span>(<span itemprop="issueNumber">Special Issue 01</span>):
<span itemprop="pagination">7--19</span></em> </span>(<em><span>Februar 2008<meta content="Februar 2008" itemprop="datePublished"/></span></em>)Wed Dec 05 18:02:57 CET 2012The Knowledge Engineering ReviewfebSpecial Issue 017--19Situational reasoning for task-oriented mobile service recommendation232008complex knowledge mobile recommender service situation task We study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability. Referral Web: combining social networks and collaborative filteringhttps://puma.uni-kassel.de/bibtex/2832d16a8c86e769c7ac9ace5381f757e/jaeschkejaeschke2012-12-05T16:50:59+01:00collaborative filtering hybrid network recommender social stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Henry Kautz" itemprop="url" href="/author/Henry%20Kautz"><span itemprop="name">H. Kautz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bart Selman" itemprop="url" href="/author/Bart%20Selman"><span itemprop="name">B. Selman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mehul Shah" itemprop="url" href="/author/Mehul%20Shah"><span itemprop="name">M. Shah</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>Communications of the ACM</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">40 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">63--65</span></em> </span>(<em><span>März 1997<meta content="März 1997" itemprop="datePublished"/></span></em>)Wed Dec 05 16:50:59 CET 2012New York, NY, USACommunications of the ACMmar363--65Referral Web: combining social networks and collaborative filtering401997collaborative filtering hybrid network recommender social stair Personalized Conversational Case-Based Recommendationhttps://puma.uni-kassel.de/bibtex/25f020ff49356f96d199ac029d1b7c81a/jaeschkejaeschke2012-12-05T15:33:23+01:00cbr personalization recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mehmet H. Göker" itemprop="url" href="/author/Mehmet%20H.%20G%c3%b6ker"><span itemprop="name">M. Göker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Cynthia A. Thompson" itemprop="url" href="/author/Cynthia%20A.%20Thompson"><span itemprop="name">C. Thompson</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Advances in Case-Based Reasoning</span>, </em><em>Volume 1898 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2000<meta content="2000" itemprop="datePublished"/></span></em>)Wed Dec 05 15:33:23 CET 2012Berlin/HeidelbergAdvances in Case-Based Reasoning99--111Lecture Notes in Computer SciencePersonalized Conversational Case-Based Recommendation18982000cbr personalization recommender In this paper, we describe the Adaptive Place Advisor, a user adaptive, conversational recommendation system designed to help users decide on a destination, specifically a restaurant. We view the selection of destinations as an interactive, conversational process, with the advisory system inquiring about desired item characteristics and the human responding. The user model, which contains preferences regarding items, attributes, values, value combinations, and diversification, is also acquired during the conversation. The system enhances the user’s requirements with the user model and retrieves suitable items from a case-base. If the number of items found by the system is unsuitable (too high, too low) the next attribute to be constrained or relaxed is selected based on the information gain associated with the attributes. We also describe the current status of the system and future work.Online Controlled Experiments: Introduction, Learnings, and Humbling Statisticshttps://puma.uni-kassel.de/bibtex/2aa31e13651d5d1eab42e449e55a0e745/jaeschkejaeschke2012-09-20T09:59:05+02:002012 amazon bing evaluation experiment industry keynote online recommender recsys statistics <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ron Kohavi" itemprop="url" href="/author/Ron%20Kohavi"><span itemprop="name">R. Kohavi</span></a></span>. </span>(<em><span>12.09.2012<meta content="12.09.2012" itemprop="datePublished"/></span></em>)Thu Sep 20 09:59:05 CEST 2012sepOnline Controlled Experiments: Introduction, Learnings, and Humbling StatisticsIndustry keynote at ACM Recommender Systems20122012 amazon bing evaluation experiment industry keynote online recommender recsys statistics 12The web provides an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using controlled experiments (e.g., A/B tests and their generalizations). Whether for front-end user-interface changes, or backend recommendation systems and relevance algorithms, online controlled experiments are now utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher’s experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons. We provide an introduction, share real examples, key learnings, cultural challenges, and humbling statistics. Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendationhttps://puma.uni-kassel.de/bibtex/264bf590675a833770b7d284871435a8d/jaeschkejaeschke2012-09-06T21:54:12+02:002012 bookmarking collaborative 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>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Thu Sep 06 21:54:12 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep9--16Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation 20122012 bookmarking collaborative 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.Extending FolkRank with Content Datahttps://puma.uni-kassel.de/bibtex/2b16dabcd7e17b673c34608ac820ce3c7/jaeschkejaeschke2012-09-06T21:52:34+02:002012 bookmarking collaborative folkrank 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="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>, <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="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <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>. </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">1--8</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Thu Sep 06 21:52:34 CEST 2012New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep1--8Extending FolkRank with Content Data20122012 bookmarking collaborative folkrank myown recommender social tagging Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags. Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.Pairwise interaction tensor factorization for personalized tag recommendationhttps://puma.uni-kassel.de/bibtex/210fe730b391b08031f3103f9cdbb6e1a/jaeschkejaeschke2012-06-29T12:41:22+02:00collaborative factorization folksonomy personalization recommender tag tagging tensor <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Rendle" itemprop="url" href="/author/Steffen%20Rendle"><span itemprop="name">S. Rendle</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lars Schmidt-Thieme" itemprop="url" href="/author/Lars%20Schmidt-Thieme"><span itemprop="name">L. Schmidt-Thieme</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">81--90</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>)Fri Jun 29 12:41:22 CEST 2012New York, NY, USAProceedings of the third ACM international conference on Web search and data mining81--90Pairwise interaction tensor factorization for personalized tag recommendation2010collaborative factorization folksonomy personalization recommender tag tagging tensor Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.