CHENG Si,TAO Hongcai.A Movie Recommendation Model based on Time Weights and User Behavior Sequences[J].Journal of Chengdu University of Information Technology,2022,37(03):241-247.[doi:10.16836/j.cnki.jcuit.2022.03.001]
一种融合时间权值和用户行为序列的电影推荐模型
- Title:
- A Movie Recommendation Model based on Time Weights and User Behavior Sequences
- 文章编号:
- 2096-1618(2022)03-0241-07
- Keywords:
- movie recommendation; user behavior sequence; time weight; GRU
- 分类号:
- TP391.3
- 文献标志码:
- A
- 摘要:
- 对用户历史观影行为进行研究可以给电影推荐提供意见和参考。在实际推荐应用中,随着时间的推移,用户的兴趣会发生相应的变化,而愈接近当前时刻的历史行为对当前时刻的兴趣状态的影响愈大。但已有的GRU模型只能通过历史行为推断出最终的兴趣,并未考虑到历史行为对最终兴趣的影响存在随时间衰减这一特性。文中提出TGRU改进模型将时间权值与GRU模型相融合,以此获得更加精确的兴趣表达。此外,使用AUGRU模型捕捉与最终兴趣相关的那些兴趣的演化过程,从而预测用户未来的兴趣。基于上述成果与分析,最终提出一种融合时间权值和用户行为序列的电影推荐模型——MRTUB,该模型较为全面地考虑了用户兴趣的提取及演化过程。实验结果表明,所提出的MRTUB电影推荐模型相比于GRU的各种叠加模型而言,效果更优。
- Abstract:
- The research on the users’ historical movie viewing behaviors can provide some opinions and references for movie recommendation. In the practical recommendation application, with the passage of time, the interests of users will change accordingly, and the closer the historical behavior to the current moment, the greater the influence on the interest state of the current moment. However, the existing GRU model can only infer the final interest through historical behaviors, without taking into account the characteristic that the influence of historical behavior on the final interest decays with time. The improved TGRU model first proposed in this paper combines the time weight with the GRU model to obtain a more accurate expression of interest. Furthermore, the AUGRU model is used to capture the evolution process of those interests related to the final interest, thereby predicting the users’ future interest. Based on the above results and analysis, this paper finally proposes a movie recommendation model, i.e., MRTUB, which integrates time weights and user behavior sequences. The experimental results show that the MRTUB movie recommendation model proposed in this paper has better effect than various superposition models of GRU.
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备注/Memo
收稿日期:2022-03-15
基金项目:国家自然科学基金资助项目(61806170)