Introducing affective agents in recommendation systems based on relational data clustering
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Cluster ensembles in collaborative filtering recommendation
Applied Soft Computing
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Recommender systems provide a solution to the problem of successful information searching in the reservoirs of the Internet by providing individualized recommendations. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. In this work a clustering approach based on semi-supervised learning is proposed. The method is then used to construct a recommender system for movies that combines contentbased and collaborative information. The proposed system was tested on the MovieLens data set, yielding recommendations of high accuracy.