Agent-based collaborative filtering based on fuzzy recommendations

Recommender systems intend to provide suggestions based on the opinion of several sources of information. But personalised suggestions based on past user’s likes and dislikes require a distributed approach. In this way, agents may automatically collect recommendations from other agents applying personal criteria in order to determine whether an item is recommended to the user or not. The application of agent technology to recommending problem has been tested before by researchers from the M.I.T., Univ. North Carolina, and the Spanish Research Institute. In this paper, we present a new, elegant and effective way to combine vague and subjective opinions to make recommendations using fuzzy logic. We have adapted real data on evaluations of movies (from the site MovieLens) to compare our proposal with the predecessors. The experimental results obtained show, using ROC curves and cost analysis, how our approach performs better than some other distributed collaborative filtering methods applied to provide personalised recommendations.

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