| 摘要: |
| 自监督学习可以从原始数据中挖掘自监督信号, 在提高推荐性能方面蕴含着巨大的潜力. 然而, 目前基于自监督学习的推荐方法存在两个关键的挑战. 首先, 大多数自监督推荐模型采用对同一节点随机扰动的方式, 将生成的不同结果作为自监督信号, 然而, 由于推荐系统中存在着广泛的同质性, 这种方式会忽略邻居节点信息, 影响推荐性能. 其次, 用户-物品之间的历史交互信息以及用户与用户之间的社交关系信息是目前基于自监督学习推荐模型关注的焦点, 而忽略了物品之间的内在联系, 同样会导致产生的自监督信号不够充分. 基于这些挑战, 提出一种基于多视角的自监督推荐方法, 分别从偏好视角、用户视角、物品视角考虑, 进而使用多视图共同训练的自监督学习方法, 结合用户之间的社交关系、物品之间的类别关系、用户-物品之间的历史交互信息, 充分挖掘自监督信号. 在3个真实的公开数据集上进行实验, 实验结果验证了基于多视角的自监督学习方法在改进推荐性能方面是有效的. |
| 关键词: 自监督学习 推荐系统 多视角 |
| DOI：10.13328/j.cnki.jos.007419 |
| 分类号:TP18 |
| 基金项目:中央高校基本科研业务费专项资金(2023JBZY035) |
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| Self-supervised Recommendation Method Based on Multiple Views |
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WANG Dan-Dan1, LIU Hai-Yang1, LIU Zhuang2, YUAN Ji-Dong1
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1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2.School of Computer Science and Engineering, Beihang University, Beijing 100191, China
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| Abstract: |
| Self-supervised learning (SSL) can extract self-supervised signals from raw data, which holds great potential for improving recommendation performance. However, two key challenges remain in current self-supervised learning-based recommendation methods. First, most self-supervised recommendation models apply random perturbations to the same node and use the generated different results as self-supervised signals. However, due to the extensive homogeneity in recommendation systems, this method ignores the information from neighboring nodes, which affects the recommendation performance. Secondly, while historical interaction information between users and items as well as the social relationship information between users are the focus of current self-supervised learning-based recommendation models, the internal relationships between items are often neglected. This also leads to insufficient self-supervised signals. Based on these challenges, a self-supervised recommendation method based on multiple views is proposed. This method considers perspectives from preference, user, and item, and employs a multi-view joint training approach for self-supervised learning. By combining the social relationships between users, the category relationships between items, and the historical interaction information between users and items, self-supervised signals are fully extracted. Experiments conducted on three real public datasets validate that the proposed multi-view-based self-supervised learning method is effective in improving recommendation performance. |
| Key words: self-supervised learning recommender system multi-view |
