hide
Free keywords:
-
Abstract:
Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user
preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information
such as past user-item ratings as well as attributes of items or users to learn a prediction
function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows
simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron
learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.
