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Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit Feedback
2022
Computational Intelligence and Neuroscience
Explicit feedback and implicit feedback are two important types of heterogeneous data for constructing a recommendation system. The combination of the two can effectively improve the performance of the recommendation system. However, most of the current deep learning recommendation models fail to fully exploit the complementary advantages of two types of data combined and usually only use binary implicit feedback data. Thus, this paper proposes a neural matrix factorization recommendation
doi:10.1155/2022/9593957
pmid:35047036
pmcid:PMC8763527
fatcat:vwzwthw64jesrflazgxiy6gwta