The Wisdom of the Few A Collaborative Filtering Approach Based on Expert Opinions from the Web Xavier Amatriain Neal Lathia Josep M. Pujol Telefonica Research Dept. of Computer Science Telefonica Research Via Augusta, 177 University College of London Via Augusta, 177 Barcelona 08021, Spain Gower Street Barcelona 08021, Spain
[email protected] London WC1E 6BT, UK
[email protected] [email protected] Haewoon Kwak Nuria Oliver KAIST Telefonica Research Computer Science Dept. Via Augusta, 177 Kuseong-dong, Yuseong-gu Barcelona 08021, Spain Daejeon 305-701, Korea
[email protected] [email protected] ABSTRACT 1. INTRODUCTION Nearest-neighbor collaborative filtering provides a successful Collaborative filtering (CF) is the current mainstream ap- means of generating recommendations for web users. How- proach used to build web-based recommender systems [1]. ever, this approach suffers from several shortcomings, in- CF algorithms assume that in order to recommend items cluding data sparsity and noise, the cold-start problem, and to users, information can be drawn from what other similar scalability. In this work, we present a novel method for rec- users liked in the past. The Nearest Neighbor algorithm, ommending items to users based on expert opinions. Our for instance, does so by finding, for each user, a number of method is a variation of traditional collaborative filtering: similar users whose profiles can then be used to predict rec- rather than applying a nearest neighbor algorithm to the ommendations. However, defining similarity between users user-rating data, predictions are computed using a set of ex- is not an easy task: it is limited by the sparsity and noise in pert neighbors from an independent dataset, whose opinions the data and is computationally expensive.