期刊论文

中文

数据分析与知识发现

2096-3467

2024

8

01

80-89

10.11925/infotech.2096-3467.2022.1162

本校为第一机构

[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,构建用户一项目评分矩阵,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(1.003 1)和RMSE(1.216 0)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...

[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...