A visual interface for social information filtering
27T17:25:18Z November 2009
Collaborative or “Social” filtering has been successfully deployed over the years as a technique for analysing 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.
Science Foundation Ireland
Type of Material
IEEE Computer Society
Copyright (Published Version)
2009 by The Institute of Electrical and Electronics Engineers, Inc.
Subject – LCSH
Recommender systems (Information filtering)
Status of Item
Proceedings : 12th IEEE International Conference on Computational Science and Engineering : CSE 2009 : Vol. 4
Paper presented at the 2009 IEEE International Conference on Social Computing (SocialCom 2009) in conjunction with the 2009 IEEE International Conference on Social Computing, 29th - 31st August 2009, Vancouver, Canada
This item is made available under a Creative Commons License