[Objective] This paper proposes a new method to improve the collaborative filtering algorithm based on explicit feedbacks, aiming to address data sparsity and user selection bias issues. [Methods] First, we retrieved the negative preferences of users who have seen the items but did not interact with them. Then, we measured the visibility of items along with user activity, item popularity and time factors. Third, we introduced the concept of pre-use preferences to construct a weighted matrix factorization model based on user time point visibility. Finally, we ide.pngied items that users were not interested in, and marked them with low values. [Results] We examined our model with the MovieLens datasets, and found the recommendation accuracy of ItemCF and BiasSVD increased by an average of 2 to 2.5 times. [Limitations] There may be empirical bias in modeling pre-use preferences based on the users’ negative preferences from the “seen-but-not-interacted items”. [Conclusions] The proposed model could effectively reduce the impacts of data sparsity and user selection bias, and make accurate recommendation results.
石磊, 李树青. 基于用户时点可见性的无趣项挖掘及协同过滤推荐方法*[J]. 数据分析与知识发现, 2022, 6(5): 64-76.
Shi Lei, Li Shuqing. Mining Uninteresting Items with Visibility of User Time Points and Collaborative Filtering Recommendation Method. Data Analysis and Knowledge Discovery, 2022, 6(5): 64-76.
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