Abstract
A collaborative filtering hybrid recommendation algorithm based on the idea of optimal combination prediction (BEST-CF) is proposed, and the effectiveness of BEST-CF is verified on Movielens 100K data set using the optimal combination of the user-based collaborative filtering recommendation algorithm (User-CF) and the item-based collaborative filtering recommendation algorithm (Item-CF). Experiments results show that the BEST-CF algorithm significantly improves the rating prediction accuracy and can enhance the recommendation quality.
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