UPRec: User-aware Pre-training for sequential Recommendation

Chaojun Xiao , Ruobing Xie , Yuan Yao , Zhiyuan Liu , Maosong Sun , Xu Zhang , Leyu Lin
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引用次数: 0

Abstract

Recent years witness the success of pre-trained models to alleviate the data sparsity problem in recommender systems. However, existing pre-trained models for recommendation mainly focus on leveraging universal sequence patterns from user behavior sequences and item information, whereas ignore heterogeneous user information to capture personalized interests, which has been shown to contribute to the personalized recommendation. In this paper, we propose a simple yet effective model, called User-aware Pre-training for Recommendation (UPRec), which could flexibly encode heterogeneous user information into the sequential modeling of user behaviors. Specifically, UPRec first encodes the sequential behavior to generate user embeddings, and then jointly optimizes the model with the sequential objective and user-aware objective constructed from the user attributes and structured social graphs. Comprehensive experimental results on two real-world large-scale recommendation datasets demonstrate that UPRec can effectively enrich the user representations with user attributes and social relations and thus provide more appropriate recommendations for users.

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UPRec:顺序推荐的用户感知预培训
近年来,预训练模型在缓解推荐系统中的数据稀疏性问题方面取得了成功。然而,现有的预训练推荐模型主要关注利用来自用户行为序列和项目信息的通用序列模式,而忽略异构用户信息来捕获个性化兴趣,这已被证明有助于个性化推荐。在本文中,我们提出了一个简单而有效的模型,称为用户感知推荐预训练(UPRec),它可以灵活地将异构用户信息编码到用户行为的序列建模中。具体而言,UPRec首先对序列行为进行编码以生成用户嵌入,然后利用由用户属性和结构化社交图构建的序列目标和用户感知目标来联合优化模型。在两个真实世界的大规模推荐数据集上的综合实验结果表明,UPRec可以有效地丰富具有用户属性和社会关系的用户表示,从而为用户提供更合适的推荐。
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