Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Strategies for Effective Shilling Attacks against Recommender Systems
Privacy, Security, and Trust in KDD
Multimedia Tools and Applications
Towards three-stage recommender support for online consumers: implications from a user study
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Case study: recommending course reading materials in a small virtual learning community
International Journal of Web Based Communities
Understanding buyers' social information needs during purchase decision process
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
Beyond Recommendations: Local Review Web Sites and Their Impact
ACM Transactions on Computer-Human Interaction (TOCHI)
A preliminary analysis of vocabulary in mobile app user reviews
Proceedings of the 24th Australian Computer-Human Interaction Conference
Recommending additional study materials: binary ratings vis-à-vis five-star ratings
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
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We studied user behavior in a recommender-rich environment, Amazon online store, to see what role the algorithm-based and user-generated recommendations play in finding items of interest. We used applied ethnography, on-location interviewing and observation, to get an accurate picture of user activity. We were especially interested in the role of customer ratings and reviews and what kind of strategies users had developed for such an environment. Our results underline the need to develop recommender systems as a whole. The way the recommendations are shown affects which items get picked, and for improving the interface, it is necessary to study the whole in addition to studying the parts in isolation.