Push-Poll Recommender System: Supporting Word of Mouth

Authors:
Andrew Webster;Julita Vassileva
Affiliations:
Department of Computer Science, University of Saskatchewan, Saskatoon SK, S7N 5C9, Canada;Department of Computer Science, University of Saskatchewan, Saskatoon SK, S7N 5C9, Canada
Venue:
UM '07 Proceedings of the 11th international conference on User Modeling
Year:
2007

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Abstract

Recommender systems produce social networks as a side effect of predicting what users will like. However, the potential for these social networks to aid in recommending items is largely ignored. We propose a recommender system that works directly with these networks to distribute and recommend items: the informal exchange of information (word of mouth communication) is supported rather than replaced. The paper describes the push-poll approach and evaluates its performance at predicting user ratings for movies against a collaborative filtering algorithm. Overall, the push-poll approach performs significantly better while being computationally efficient and suitable for dynamic domains (e.g. recommending items from RSS feeds).