Incollection,
Push-Poll Recommender System: Supporting Word of Mouth User Modeling 2007
Proceedings of User Modeling 2007, volume 4511 of LNCS, chapter 31, Springer, Berlin, Heidelberg, (2007)
DOI: 10.1007/978-3-540-73078-1_31
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).
