Recommender Systems
Papers from the AAAI Workshop
Henry Kautz, Program Chair
Technical Report WS-98-08 published by The AAAI Press, Menlo Park, California
This technical report is also available in book and CD format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Call for Participation / 5
Henry Kautz
An Interface for Learning Multi-topic User Profiles from Implicit Feedback / 6
Marko Balabanovic
Recommendation as Classification: Using Social and Content-Based Information in Recommendation / 11
Chumki Basu, Haym Hirsh, and William Cohen
Recommending TV Programs: How Far Can We Get at Zero User Effort? / 16
Patrick Baudisch
Recommending Web Resources to Science Educators / 19
Marie A. Bienkowski
Learning Collaborative Information Filters / 24
Daniel Billsus and Michael J. Pazzani
A Data Warehousing Approach for Building Recommender Systems / 29
Z. Chen
Context-Based Profile Personalization / 33
Chandra Dharap
Recommender Systems for TV / 35
Duco Das and Herman ter Horst
Content + Collaboration = Recommendation / 37
Joachin Delgado and Naohiro Ishii
Interactive Interface Agents as Recommender Systems / 42
Michael Fleming and Robin Cohen
Constraint-Based Strategies for Matchmakers / 47
c
Putting Recommender Systems to Work for Organizations / 51
Natalie S. Glance
Collaborative Filtering for Web Marketing Efforts / 53
Dan R. Greening
A Framework Supporting Collaborative Filtering for Internet Information / 56
Sukumal Imudom and B. Clifford Neuman
Creating Models of Real-World Communities with ReferralWeb / 58
Henry Kautz and Bart Selman
Recommder Systems: A GroupLens Perspective / 60
Joseph A. Konstan, John Riedl, Al Borchers, and Jonathan L. Herlocker
OWL: A Recommender System for Organization-Wide Learning / 64
Frank Linton, Andy Charron, and Debbie Joy
Book Recommending Using Text Categorization with Extracted Information / 69
Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
Use of Voting Schemes to Tradeoff User Preferences / 74
Manisha Mundhe and Sandip Sen
The Decision-Theoretic Video Advisor / 76
Hien Nguyen and Peter Haddawy
Implicit Feedback for Recommender Systems / 80
Douglas W. Oard and Jinmook Kim
Decentralized Social Filtering based on Trust / 83
Tomas Olsson
InfoScout: An Active Recommender Agent / 88
M. V. Nagendra Prasad and Theodore Anagnost
Recommender Systems for Problem Solving Environments / 90
Naren Ramakrishnan, Elias N. Houstis, and John R. Rice
Personalized Driving Route Recommendations / 95
Seth Rogers and Pat Langley
Mining the Web’s Hyperlinks for Recommendations / 100
Ellen Spertus and Lynn Andrea Stein
Personal Context-aware Guidance System for Exhibition Tours / 105
Yasuyuki Sumi, Tameyuki Etani, Kaoru Kobayashi, Sidney Fels, Nicolas Simonet, and Kenji Mase
Why Recommendation is Special? / 109
Pei Wang
Clustering Methods for Collaborative Filtering / 112
Lyle H. Ungar and Dean P. Foster
AAAI Digital Library
AAAI relies on your generous support through membership and donations. If you find these resources useful, we would be grateful for your support.