RecSys 2008: Lausanne, Switzerland

• Andrei Z. Broder:
  Computational advertising and recommender systems. 1-2
  

Recommendation algorithms

• Shengchao Ding, Shiwan Zhao, Quan Yuan, Xiatian Zhang, Rongyao Fu, Lawrence D. Bergman:
  Boosting collaborative filtering based on statistical prediction errors. 3-10
  
• Yoon-Joo Park, Alexander Tuzhilin:
  The long tail of recommender systems and how to leverage it. 11-18
  
• Asela Gunawardana, Christopher Meek:
  Tied boltzmann machines for cold start recommendations. 19-26
  
• Rossano Schifanella, André Panisson, Cristina Gena, Giancarlo Ruffo:
  MobHinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networks. 27-34
  
• Guy Shani, David Maxwell Chickering, Christopher Meek:
  Mining recommendations from the web. 35-42
  

Social networks and recommenders

• Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos:
  Tag recommendations based on tensor dimensionality reduction. 43-50
  
• Valentina Zanardi, Licia Capra:
  Social ranking: uncovering relevant content using tag-based recommender systems. 51-58
  
• Werner Geyer, Casey Dugan, David R. Millen, Michael J. Muller, Jill Freyne:
  Recommending topics for self-descriptions in online user profiles. 59-66
  
• Nikhil Garg, Ingmar Weber:
  Personalized, interactive tag recommendation for flickr. 67-74
  

User studies

• Li Chen, Pearl Pu:
  A cross-cultural user evaluation of product recommender interfaces. 75-82
  
• Songhua Xu, Hao Jiang, Francis C. M. Lau:
  Personalized online document, image and video recommendation via commodity eye-tracking. 83-90
  
• Veronica Maidel, Peretz Shoval, Bracha Shapira, Meirav Taieb-Maimon:
  Evaluation of an ontology-content based filtering method for a personalized newspaper. 91-98
  

Conversational systems

• Hu Wu, Yongji Wang, Xiang Cheng:
  Incremental probabilistic latent semantic analysis for automatic question recommendation. 99-106
  
• Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, Edward Y. Chang:
  Pfp: parallel fp-growth for query recommendation. 107-114
  
• Tarik Hadzic, Barry O'Sullivan:
  Critique graphs for catalogue navigation. 115-122
  

Recommender challenges

• Mi Zhang, Neil Hurley:
  Avoiding monotony: improving the diversity of recommendation lists. 123-130
  
• Hilmi Yildirim, Mukkai S. Krishnamoorthy:
  A random walk method for alleviating the sparsity problem in collaborative filtering. 131-138
  
• Markus Zanker:
  A collaborative constraint-based meta-level recommender. 139-146
  
• Paul Resnick, Rahul Sami:
  The information cost of manipulation-resistance in recommender systems. 147-154
  
• Kenneth Bryan, Michael P. O'Mahony, Padraig Cunningham:
  Unsupervised retrieval of attack profiles in collaborative recommender systems. 155-162
  

Posters

• Marco Degemmis, Pasquale Lops, Giovanni Semeraro, Pierpaolo Basile:
  Integrating tags in a semantic content-based recommender. 163-170
  
• Alexander Brodsky, Sylvia Morgan Henshaw, Jon Whittle:
  CARD: a decision-guidance framework and application for recommending composite alternatives. 171-178
  
• Òscar Celma, Perfecto Herrera:
  A new approach to evaluating novel recommendations. 179-186
  
• Sara Drenner, Shilad Sen, Loren G. Terveen:
  Crafting the initial user experience to achieve community goals. 187-194
  
• Martijn Kagie, Michiel C. van Wezel, Patrick J. F. Groenen:
  Choosing attribute weights for item dissimilarity using clikstream data with an application to a product catalog map. 195-202
  
• Georgia Koutrika, Robert Ikeda, Benjamin Bercovitz, Hector Garcia-Molina:
  Flexible recommendations over rich data. 203-210
  
• Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, Joseph A. Konstan:
  Who predicts better?: results from an online study comparing humans and an online recommender system. 211-218
  
• Kleanthi Lakiotaki, Stelios Tsafarakis, Nikolaos F. Matsatsinis:
  UTA-Rec: a recommender system based on multiple criteria analysis. 219-226
  
• Neal Lathia, Stephen Hailes, Licia Capra:
  kNN CF: a temporal social network. 227-234
  
• Nathan Oostendorp, Paul Resnick:
  Three recommender approaches to interface controls reduction. 235-242
  
• Juan A. Recio-García, Belén Díaz-Agudo, Pedro A. González-Calero:
  Prototyping recommender systems in jcolibri. 243-250
  
• Steffen Rendle, Lars Schmidt-Thieme:
  Online-updating regularized kernel matrix factorization models for large-scale recommender systems. 251-258
  
• Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke:
  Personalized recommendation in social tagging systems using hierarchical clustering. 259-266
  
• Gábor Takács, István Pilászy, Bottyán Németh, Domonkos Tikk:
  Matrix factorization and neighbor based algorithms for the netflix prize problem. 267-274
  
• Markus Weimer, Alexandros Karatzoglou, Alex J. Smola:
  Adaptive collaborative filtering. 275-282
  

Short papers

• Arun Kumar Agrahri, Divya Anand Thattandi Manickam, John Riedl:
  Can people collaborate to improve the relevance of search results? 283-286
  
• Toine Bogers, Antal van den Bosch:
  Recommending scientific articles using citeulike. 287-290
  
• M. Benjamin Dias, Dominique Locher, Ming Li, Wael El-Deredy, Paulo J. G. Lisboa:
  The value of personalised recommender systems to e-business: a case study. 291-294
  

Doctoral symposium

• Linas Baltrunas:
  Exploiting contextual information in recommender systems. 295-298
  
• Marcos Aurélio Domingues:
  An independent platform for the monitoring, analysis and adaptation of web sites. 299-302
  
• Hendrik Drachsler, Hans G. K. Hummel, Rob Koper:
  Navigation support for learners in informal learning environments. 303-306
  
• YoungOk Kwon:
  Improving top-n recommendation techniques using rating variance. 307-310
  
• Danielle Hyunsook Lee:
  PITTCULT: trust-based cultural event recommender. 311-314
  
• Leobino Nascimento Sampaio:
  A network performance recommendation process for advanced internet applications users. 315-318
  
• Olga C. Santos:
  A recommender system to provide adaptive and inclusive standard-based support along the elearning life cycle. 319-322
  
• Erich Christian Teppan:
  Implications of psychological phenomenons for recommender systems. 323-326
  
• Akhmed Umyarov:
  Leveraging aggregate ratings for improving predictive performance of recommender systems. 327-330
  

Tutorials

• Robin D. Burke:
  Robust recommender systems. 331-332
  
• Yehuda Koren:
  Tutorial on recent progress in collaborative filtering. 333-334
  
• Gediminas Adomavicius, Alexander Tuzhilin:
  Context-aware recommender systems. 335-336