Dynamic Movie Recommendation Considering Long-Term and Short-Term Interest and Its Evolution
Liu Rui1,Chen Ye2()
1School of Information Management, Central China Normal University, Wuhan 430079, China 2School of Information Management, Nanjing University, Nanjing 210023, China
[Objective] This paper proposes a personalized dynamic recommendation model for movies. It considered the evolution of long-term interest and short-term interest, capturing the dynamic changes of users’ interests to improve the accuracy of recommendation. [Methods] Firstly, users’ interest is divided into the long-term interest and the short-term interest based on their psychological motivation. And then the model used interest rating and attention frequency to calculate the interest values. Secondly, the model combined the time window with the forgetting function to obtain the time weight. The short-term interest value and the time weight are combined to reflect the evolution of short-term interest. Finally, the model constructed a user-project scoring matrix to predict the score of target user, by integrating the movie score with the long-term and the short-term interest values. [Results] Taking the data set of Douban as an example, the score prediction error of the method was smaller overall than that of other recommendation methods, and it performed best on MAE (1.0031) and RMSE (1.2160), and the number of neighbors is 20 when reaching the optimal values of MAE and RMSE. [Limitations] The explicit feedback information and the implicit feedback information are needed to calculate long-term and short-term interest values, so the computational complexity of the proposed method is relatively high. [Conclusions] The recommendation method can accurately capture the dynamic change of user interest, effectively reduce the error of score prediction, and improve the accuracy of recommendation.
刘瑞, 陈烨. 考虑长短期兴趣及其演化的电影个性化动态推荐研究*[J]. 数据分析与知识发现, 2024, 8(1): 80-89.
Liu Rui, Chen Ye. Dynamic Movie Recommendation Considering Long-Term and Short-Term Interest and Its Evolution. Data Analysis and Knowledge Discovery, 2024, 8(1): 80-89.
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