Table of Contents

• Computational advertising and recommender systemsAndrei Z. Broder. 1-2 [doi]
• Boosting collaborative filtering based on statistical prediction errorsShengchao Ding, Shiwan Zhao, Quan Yuan, Xiatian Zhang, Rongyao Fu, Lawrence Bergman. 3-10 [doi]
• The long tail of recommender systems and how to leverage itYoon-Joo Park, Alexander Tuzhilin. 11-18 [doi]
• Tied boltzmann machines for cold start recommendationsAsela Gunawardana, Christopher Meek. 19-26 [doi]
• MobHinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networksRossano Schifanella, André Panisson, Cristina Gena, Giancarlo Ruffo. 27-34 [doi]
• Mining recommendations from the webGuy Shani, David Maxwell Chickering, Christopher Meek. 35-42 [doi]
• Tag recommendations based on tensor dimensionality reductionPanagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos. 43-50 [doi]
• Social ranking: uncovering relevant content using tag-based recommender systemsValentina Zanardi, Licia Capra. 51-58 [doi]
• Recommending topics for self-descriptions in online user profilesWerner Geyer, Casey Dugan, David R. Millen, Michael J. Muller, Jill Freyne. 59-66 [doi]
• Personalized, interactive tag recommendation for flickrNikhil Garg, Ingmar Weber. 67-74 [doi]
• A cross-cultural user evaluation of product recommender interfacesLi Chen, Pearl Pu. 75-82 [doi]
• Personalized online document, image and video recommendation via commodity eye-trackingSonghua Xu, Hao Jiang, Francis C. M. Lau. 83-90 [doi]
• Evaluation of an ontology-content based filtering method for a personalized newspaperVeronica Maidel, Peretz Shoval, Bracha Shapira, Meirav Taieb-Maimon. 91-98 [doi]
• Incremental probabilistic latent semantic analysis for automatic question recommendationHu Wu, Yongji Wang, Xiang Cheng. 99-106 [doi]
• Pfp: parallel fp-growth for query recommendationHaoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, Edward Y. Chang. 107-114 [doi]
• Critique graphs for catalogue navigationTarik Hadzic, Barry O Sullivan. 115-122 [doi]
• Avoiding monotony: improving the diversity of recommendation listsMi Zhang, Neil Hurley. 123-130 [doi]
• A random walk method for alleviating the sparsity problem in collaborative filteringHilmi Yildirim, Mukkai S. Krishnamoorthy. 131-138 [doi]
• A collaborative constraint-based meta-level recommenderMarkus Zanker. 139-146 [doi]
• The information cost of manipulation-resistance in recommender systemsPaul Resnick, Rahul Sami. 147-154 [doi]
• Unsupervised retrieval of attack profiles in collaborative recommender systemsKenneth Bryan, Michael O Mahony, Padraig Cunningham. 155-162 [doi]
• Integrating tags in a semantic content-based recommenderMarco Degemmis, Pasquale Lops, Giovanni Semeraro, Pierpaolo Basile. 163-170 [doi]
• CARD: a decision-guidance framework and application for recommending composite alternativesAlexander Brodsky, Sylvia Morgan Henshaw, Jon Whittle. 171-178 [doi]
• A new approach to evaluating novel recommendationsÒscar Celma, Perfecto Herrera. 179-186 [doi]
• Crafting the initial user experience to achieve community goalsSara Drenner, Shilad Sen, Loren G. Terveen. 187-194 [doi]
• Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog mapMartijn Kagie, Michiel C. van Wezel, Patrick J. F. Groenen. 195-202 [doi]
• Flexible recommendations over rich dataGeorgia Koutrika, Robert Ikeda, Benjamin Bercovitz, Hector Garcia-Molina. 203-210 [doi]
• Who predicts better?: results from an online study comparing humans and an online recommender systemVinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, Joseph A. Konstan. 211-218 [doi]
• UTA-Rec: a recommender system based on multiple criteria analysisKleanthi Lakiotaki, Stelios Tsafarakis, Nikolaos F. Matsatsinis. 219-226 [doi]
• kNN CF: a temporal social networkNeal Lathia, Stephen Hailes, Licia Capra. 227-234 [doi]
• Three recommender approaches to interface controls reductionNathan Oostendorp, Paul Resnick. 235-242 [doi]
• Prototyping recommender systems in jcolibriJuan A. Recio-García, Belén Díaz-Agudo, Pedro A. González-Calero. 243-250 [doi]
• Online-updating regularized kernel matrix factorization models for large-scale recommender systemsSteffen Rendle, Lars Schmidt-Thieme. 251-258 [doi]
• Personalized recommendation in social tagging systems using hierarchical clusteringAndriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke. 259-266 [doi]
• Matrix factorization and neighbor based algorithms for the netflix prize problemGábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk. 267-274 [doi]
• Adaptive collaborative filteringMarkus Weimer, Alexandros Karatzoglou, Alex J. Smola. 275-282 [doi]
• Can people collaborate to improve the relevance of search results?Arun Kumar Agrahri, Divya Anand Thattandi Manickam, John Riedl. 283-286 [doi]
• Recommending scientific articles using citeulikeToine Bogers, Antal van den Bosch. 287-290 [doi]
• The value of personalised recommender systems to e-business: a case studyM. Benjamin Dias, Dominique Locher, Ming Li, Wael El-Deredy, Paulo J. G. Lisboa. 291-294 [doi]
• Exploiting contextual information in recommender systemsLinas Baltrunas. 295-298 [doi]
• An independent platform for the monitoring, analysis and adaptation of web sitesMarcos Aurélio Domingues. 299-302 [doi]
• Navigation support for learners in informal learning environmentsHendrik Drachsler, Hans G. K. Hummel, Rob Koper. 303-306 [doi]
• Improving top-n recommendation techniques using rating varianceYoungOk Kwon. 307-310 [doi]
• PITTCULT: trust-based cultural event recommenderDanielle Hyunsook Lee. 311-314 [doi]
• A network performance recommendation process for advanced internet applications usersLeobino Nascimento Sampaio. 315-318 [doi]
• A recommender system to provide adaptive and inclusive standard-based support along the elearning life cycleOlga C. Santos. 319-322 [doi]
• Implications of psychological phenomenons for recommender systemsErich Christian Teppan. 323-326 [doi]
• Leveraging aggregate ratings for improving predictive performance of recommender systemsAkhmed Umyarov. 327-330 [doi]
• Robust recommender systemsRobin D. Burke. 331-332 [doi]
• Tutorial on recent progress in collaborative filteringYehuda Koren. 333-334 [doi]
• Context-aware recommender systemsGediminas Adomavicius, Alexander Tuzhilin. 335-336 [doi]