Weighting strategies for a recommender system using item clustering based on genres

An original clustering approach for recommender systems.The approach is based on item metadata informations (item genres).Items are clustered in several clusters.Weighting strategies are used to combine clusters evaluations.MAE is improved between 0.3 and 1.8% and RMSE between 4.7 and 9.8%. Recommender systems are effective to identify items that could interest clients on e-commerce web sites or predict evaluations that people could give to items such as movies. In this context, clustering can be used to improve predictions or to reduce computational time. In this paper, we present a clustering approach based on item metadata informations. Evaluations are clustered according to item genre. As items can have several genres, evaluations can be placed in several clusters. Each cluster provides its own rating prediction and weighting strategies are then used to combine these results in one evaluation. Coupled with an existing collaborative filtering recommender system and applied on Yahoo! and MovieLens datasets, our method improves the MAE between 0.3 and 1.8%, and the RMSE between 4.7 and 9.8%.

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