Multi-Interest Aware Sequential Recommender System Based on Contrastive Learning

被引：0
|
作者：
基于对比学习的多兴趣感知序列推荐系统
机构：
[1] College of Computer Science, Sichuan University, Chengdu
来源：
Ju, Shenggen (jsg@scu.edu.cn) | 1730年 / Science Press卷 / 61期
基金：
中国国家自然科学基金;
关键词：
capsule network; global preference; local preference; multiple interests; sequential recommendation;
D O I：
10.7544/issn1000-1239.202330622
中图分类号：
学科分类号：
摘要：
Recent advancements in the field of sequential recommender have focused on refining user interests through various methods, such as clustering historical interactions or utilizing graph convolutional neural networks to capture multi-level correlations among interactions. However, while these approaches have significantly advanced the field, they often overlook the interactions between users with similar behavioral patterns and the impact of irregular time intervals within interaction sequences on user interests. Based on the above problems, a multi-interest aware sequential recommender model (MIRec) based on contrastive learning is proposed. This model takes into account both local preference information, including item dependence and location dependence within a sequence, and global preference information obtained through a graph information aggregation mechanism among similar users. The user representations, which incorporate both local and global preferences, are fed into a capsule network to learn multi-interest representations within the user interaction sequence. Subsequently, the user’s historical interaction sequences are brought closer to enhanced interaction sequences through contrastive learning. This process results in the generation of a user’s multi-interest representation that is insensitive to time intervals, ultimately leading to more accurate recommendations for users. The effectiveness of this model is verified on two real datasets, and the experimental results verify the effectiveness of the model. © 2024 Science Press. All rights reserved.
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收藏
页码：1730 / 1740
页数：10
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