TY  - CONF
ID  - www200968
UR  - http://www2009.eprints.org/68/
A1  - Sen, Shilad
A1  - Vig, Jesse
A1  - Riedl, John
Y1  - 2009/04//
N2  - Tagging has emerged as a powerful mechanism that enables users to ?nd, organize, and understand online entities. Recommender systems similarly enable users to ef?ciently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the ?exibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users? preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users? interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users? ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to ?exible recommender systems that leverage the characteristics of items users ?nd most important.
TI  - Tagommenders: Connecting Users to Items through Tags
SP  - 671
M2  - Madrid, Spain
AV  - public
EP  - 671
T2  - 18th International World Wide Web Conference
ER  -