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). © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Webster, A., & Vassileva, J. (2007). Push-poll recommender system: Supporting word of mouth. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4511 LNCS, pp. 278–287). Springer Verlag. https://doi.org/10.1007/978-3-540-73078-1_31
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