Effects of User Tastes on Personalized Recommendation

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Executive Summary

In this paper, based on a weighted projection of the user-object bipartite network, the authors study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. They argue that the initial recommendation power located on the objects should be determined by both of their degree and the users' tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated.

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