Intelligent information-sharing systems
Communications of the ACM
Object lens: a “spreadsheet” for cooperative work
CSCW '88 Proceedings of the 1988 ACM conference on Computer-supported cooperative work
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Agents that reduce work and information overload
Communications of the ACM
How might people interact with agents
Communications of the ACM
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Supporting situated actions in high volume conversational data situations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Personal choice point: helping users visualize what it means to buy a BMW
Proceedings of the 8th international conference on Intelligent user interfaces
Communities through Time: Using History for Social Navigation
Community Computing and Support Systems, Social Interaction in Networked Communities [the book is based on the Kyoto Meeting on Social Interaction and Communityware, held in Kyoto, Japan, in June 1998]
The VLDB Journal — The International Journal on Very Large Data Bases
Designing information spaces
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Flash forums and forumReader: navigating a new kind of large-scale online discussion
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Information Systems (TOIS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evaluation of attribute-aware recommender system algorithms on data with varying characteristics
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Combining demographic data with collaborative filtering for automatic music recommendation
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
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Collaborative filetering attempts to alleviate information overload by offering recommendations on whether information is valuable based on the opinions of those who have already evaluated it. Usenet news is an information source whose value is being severely diminished by the volume of low-quality and uninteresting information posted in its newsgroups. The GroupLens system applies collaborative filtering to Usenet news to demonstrate how we can restore the value of Usenet news by sharing our judgements of articles, with our identities protected by pseudonyms. This paper extends the original GroupLens work by reporting on a significantly enhanced system and the results of a seven week trial with 250 users and over 20,000 news articles. GroupLens has an open and flexible architecture that allows easy integration of new newsreader clients and ratings bureaus. We show ratings and prediction profiles for three news-groups, and assess the accuracy of the predictions.