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
GroupLens: applying collaborative filtering to Usenet news
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
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Web user de-identification in personalization
Proceedings of the 17th international conference on World Wide Web
Behavioral experiments in networked trade
Proceedings of the 9th ACM conference on Electronic commerce
A visual interface for critiquing-based recommender systems
Proceedings of the 9th ACM conference on Electronic commerce
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Shopping for products you don't know you need
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 12th ACM conference on Electronic commerce
Recommender systems at the long tail
Proceedings of the fifth ACM conference on Recommender systems
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In this paper, we introduce the method tagging substitute-complement attributes on miscellaneous recommending relations, and elaborate how this step contributes to electronic merchandising. There are already decades of works in building recommender systems. Steadily outperforming previous algorithms is difficult under the conventional framework. However, in real merchandising scenarios, we find describing the weight of recommendation simply as a scalar number is hardly expressive, which hinders the further progress of recommender systems. We study a large log of user browsing data, revealing the typical substitute complement relations among items that can further extend recommender systems in enriching the presentation and improving the practical quality. Finally, we provide an experimental analysis and sketch an online prototype to show that tagging attributes can grant more intelligence to recommender systems by differentiating recommended candidates to fit respective scenarios.