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ORCIDRecSys 2008: Lausanne, Switzerland
- Pearl Pu, Derek G. Bridge, Bamshad Mobasher, Francesco Ricci:
Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, October 23-25, 2008. ACM 2008, ISBN 978-1-60558-093-7 - 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, Alexander 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
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