考虑长短期兴趣及其演化的电影个性化动态推荐研究

Data Analysis and Knowledge Discovery Dynamic Movie Recommendation Considering Long-Term and Short-Term Interest and Its Evolution

【目的】提出一种考虑长短期兴趣及其演化的电影个性化动态推荐方法,捕捉用户兴趣动态变化以提高推荐准确度。【方法】首先,基于观影心理动机将用户兴趣分为长期兴趣和短期兴趣,利用兴趣评分与关注频率计算长短期兴趣值;其次,利用时间窗口与遗忘曲线函数获取时间权重,结合短期兴趣值与时间权重拟合短期兴趣的演化规律;最后,将电影评分与长短期兴趣值相融合,构建用户-项目评分矩阵,预测目标用户评分。【结果】以豆瓣网数据集为例,所提方法的评分预测误差与其他推荐方法相比整体偏小,在评估指标MAE(1.0031)和RMSE(1.2160)上表现最优,达到MAE和RMSE最优值时所需邻居数(20)最少。【局限】由于要结合显式反馈信息与隐式反馈信息共同计算长短期兴趣值,因此所提方法的计算复杂度较高。【结论】所提方法能够准确捕捉用户兴趣的动态变化,有效降低评分预测误差,提高推荐准确度。

[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 longterm 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.