In the present age of information overload, it is becoming increasingly harder to find relevant content. Recommender systems have been introduced to help people deal with these vast amounts of information and have been widely used in research as well as e-commerce applications. In this paper, we propose several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations. We then empirically demonstrate how these approaches work with various recommendation techniques. We also show how these approaches can generate more personalized recommendations, as measured by the coverage metric. As a result, users can be given a better control to choose whether to receive recommendations with higher coverage or higher accuracy.
|Original language||English (US)|
|Number of pages||6|
|State||Published - Jan 1 2007|
|Event||17th Workshop on Information Technologies and Systems, WITS 2007 - Montreal, QC, Canada|
Duration: Dec 8 2007 → Dec 9 2007
|Other||17th Workshop on Information Technologies and Systems, WITS 2007|
|Period||12/8/07 → 12/9/07|