RecSys 2007: Minneapolis, MN, USA

Privacy and trust

• Neal Lathia, Stephen Hailes, Licia Capra:
  Private distributed collaborative filtering using estimated concordance measures. 1-8
  
• Shlomo Berkovsky, Yaniv Eytani, Tsvi Kuflik, Francesco Ricci:
  Enhancing privacy and preserving accuracy of a distributed collaborative filtering. 9-16
  
• Paolo Massa, Paolo Avesani:
  Trust-aware recommender systems. 17-24
  
• Paul Resnick, Rahul Sami:
  The influence limiter: provably manipulation-resistant recommender systems. 25-32
  

Algorithms: collaborative filtering

• Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci:
  Distributed collaborative filtering with domain specialization. 33-40
  
• Amelie Anglade, Marco Tiemann, Fabio Vignoli:
  Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems. 41-48
  
• Bhaskar Mehta, Thomas Hofmann, Wolfgang Nejdl:
  Robust collaborative filtering. 49-56
  
• Jiyong Zhang, Pearl Pu:
  A recursive prediction algorithm for collaborative filtering recommender systems. 57-64
  

User issues in recommender systems

• Derek G. Bridge, Francesco Ricci:
  Supporting product selection with query editing recommendations. 65-72
  
• Verus Pronk, Wim F. J. Verhaegh, Adolf Proidl, Marco Tiemann:
  Incorporating user control into recommender systems based on naive bayesian classification. 73-80
  
• Quang Nhat Nguyen, Francesco Ricci:
  Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations. 81-88
  
• Paolo Viappiani, Pearl Pu, Boi Faltings:
  Conversational recommenders with adaptive suggestions. 89-96
  

Algorithms: learning

• Sandra Clara Gadanho, Nicolas Lhuillier:
  Addressing uncertainty in implicit preferences. 97-104
  
• Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke:
  Robustness of collaborative recommendation based on association rule mining. 105-112
  
• Nima Taghipour, Ahmad Kardan, Saeed Shiry Ghidary:
  Usage-based web recommendations: a reinforcement learning approach. 113-120
  
• An-Te Nguyen, Nathalie Denos, Catherine Berrut:
  Improving new user recommendations with rule-based induction on cold user data. 121-128
  

Research short papers

• Ming Li, M. Benjamin Dias, Wael El-Deredy, Paulo J. G. Lisboa:
  A probabilistic model for item-based recommender systems. 129-132
  
• Michael P. O'Mahony, Barry Smyth:
  A recommender system for on-line course enrolment: an initial study. 133-136
  
• Juha Leino, Kari-Jouko Räihä:
  Case amazon: ratings and reviews as part of recommendations. 137-140
  
• Toine Bogers, Antal van den Bosch:
  Comparing and evaluating information retrieval algorithms for news recommendation. 141-144
  
• Neil Rubens, Masashi Sugiyama:
  Influence-based collaborative active learning. 145-148
  
• Tavi Nathanson, Ephrat Bitton, Kenneth Y. Goldberg:
  Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering. 149-152
  
• Nava Tintarev, Judith Masthoff:
  Effective explanations of recommendations: user-centered design. 153-156
  
• Andi Winterboer, Johanna D. Moore:
  Evaluating information presentation strategies for spoken recommendations. 157-160
  
• Akhmed Umyarov, Alexander Tuzhilin:
  Leveraging aggregate ratings for better recommendations. 161-164
  
• F. Maxwell Harper, Shilad Sen, Dan Frankowski:
  Supporting social recommendations with activity-balanced clustering. 165-168
  
• Li Chen, Pearl Pu:
  The evaluation of a hybrid critiquing system with preference-based recommendations organization. 169-172
  
• Andrew Webster, Julita Vassileva:
  The keepup recommender system. 173-176
  
• Marco Tiemann, Steffen Pauws:
  Towards ensemble learning for hybrid music recommendation. 177-178
  
• Jill Freyne, Rosta Farzan, Maurice Coyle:
  Toward the exploitation of social access patterns for recommendation. 179-182
  

Practice/industry track abstracts

• Nishikant Kapoor, Jilin Chen, John T. Butler, Gary C. Fouty, James A. Stemper, John Riedl, Joseph A. Konstan:
  Techlens: a researcher's desktop. 183-184
  
• Joseph F. McCarthy:
  The challenges of recommending digital selves in physical spaces. 185-186
  

Doctoral symposium

• Justin Donaldson:
  A hybrid social-acoustic recommendation system for popular music. 187-190
  
• Xin Fu:
  Evaluating sources of implicit feedback in web searches. 191-194
  
• Fabiana Lorenzi:
  A multiagent knowledge-based recommender approach with truth maintenance. 195-198
  
• Mike Radmacher:
  Elicitation of profile attributes by transparent communication. 199-202
  
• Nava Tintarev:
  Explanations of recommendations. 203-206
  
• Riina Vuorikari:
  Can social information retrieval enhance the discovery and reuse of digital educational content? 207-210