DNCOCO'10 Proceedings of the 9th WSEAS international conference on Data networks, communications, computers
Power to the people: exploring neighbourhood formations in social recommender system
Proceedings of the fifth ACM conference on Recommender systems
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
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Collaborative or "Social" filtering has been successfully deployed over the years as a technique for analyzing large amounts of user-preference knowledge to predict interesting items for an individual user. The black-box nature of most collaborative filtering (CF) applications leave the user wondering how the system arrived at its recommendation. In this paper we introduce PeerChooser, a collaborative recommender system with an interactive interface which provides the user not only an explanation of the recommendation process, but the opportunity to manipulate a graph of their peers at varying levels of granularity, to reflect aspects of their current requirements. PeerChooser's prediction component reads directly from the graph to yield the same results as a benchmark recommendation algorithm. Users then improve on these predictions by tweaking the graph in various ways. PeerChooser compares favorably against the benchmark in live evaluations and equally well in automated accuracy tests.