Shulin Cheng, Wanyan Wang, Shan Yang, Xiufang Cheng, Journal of Information Processing Systems Vol. 17, No. 3, pp. 462-472, Jun. 2021  

https://doi.org/10.3745/JIPS.01.0076
Keywords: Collaborative Filtering, data sparsity, Fusion Filling, Preference Matrix, Recommender System
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Abstract

With an increase in the scale of recommender systems, users’ rating data tend to be extremely sparse. Some methods have been utilized to alleviate this problem; nevertheless, it has not been satisfactorily solved yet. Therefore, we propose an effective pre-rating method based on users’ dichotomous preferences and average ratings fusion. First, based on a user–item ratings matrix, a new user-item preference matrix was constructed to analyze and model user preferences. The items were then divided into two categories based on a parameterized dynamic threshold. The missing ratings for items that the user was not interested in were directly filled with the lowest user rating; otherwise, fusion ratings were utilized to fill the missing ratings. Further, an optimized parameter λ was introduced to adjust their weights. Finally, we verified our method on a standard dataset. The experimental results show that our method can effectively reduce the prediction error and improve the recommendation quality. As for its application, our method is effective, but not complicated.

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