Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
PHOAKS: a system for sharing recommendations
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A vector space model for automatic indexing
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Insert movie reference here: a system to bridge conversation and item-oriented web sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Conversation pivots and double pivots
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Interaction-based collaborative filtering methods for recommendation in online dating
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
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Conversation double pivots recommend target items related to a source item, based on co-mentions of source and target items in online forums. We deployed several variants on the drupal.org site that supports the Drupal open source community, and assessed them through clickthrough rates. A similarity metric based on correlation of mentions rather than mere co-occurrence reduced the problem of over-recommending the most popular modules, but additional corrections for recency and uniqueness of mentions were not helpful. Detection of more module mentions in conversations dramatically improved the quality of recommendations, even though the detection algorithm then had more false positives. Recommendations based on conversation co-mention were more effective than those based on co-installation, because co-installation data only led to recommendations of complementary modules and not substitutes. Recommendations based on co-mention were more effective than those based on text similarity matching for navigating from the most popular modules, but less effective than text matching for less popular modules.