Abstract—The current collaborative filtering recommendation method using energy distance only focuses on the relationship between the user and the user, between the user group and the user group. This method has not yet considered the relationship between the item and the item. In this article, we mainly focus on proposing an item-based collaborative filtering model using the energy distance. The proposed model is evaluated on two popular datasets Jester5k and MovieLens100k. Besides, the proposed model is also compared with two item-based collaborative filtering models using the Cosine and Pearson measures. The experimental results have shown that the proposed model is better than two compared models.
 
Keywords—item-based, energy distance, collaborative filtering, incompatibility matrix, recommendation system


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