Abstract The rating matrix is sparse, and the traditional user-based collaborative filtering cannot provide high-precision satisfactory recommendations for target users. Based on this situation, a hybrid filling collaborative filtering (HFCF) is proposed to alleviate the problem of data sparsity. From the perspective of the item, the sparse matrix is filled according to the rating information of the similar items. And from the viewpoint of users, the neighborhood of the target users is calculated according to the filled matrix. The items with the largest number of common ratings are selected to further fill the matrix. Experiments on two real datasets indicate that the proposed algorithm effectively improves the recommendation precision and relieves the data sparsity problem without any additional information.
Fund:Supported by National Natural Science Foundation of China(No.61976109), Natural Science Foundation of Liaoning Province(No.20180550542), Dalian Science and Technology Innovation Fund(No.2018J12GX047), Dalian Key Laboratory Special Fund
Corresponding Authors:
ZHANG Zhipeng, Ph.D., lecture. His research interests include data mining and recommender system.
About author:: REN Yonggong, Ph.D., professor. His research interests include artificial intelligence and data mining; WANG Siyu, master student. Her research interests include artificial intelligence and data mining.
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2020, Vol. 33 
