ID-Agnostic User Behavior Pre-training for Sequential Recommendation

Shanlei Mu, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Bolin Ding
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引用次数: 6

Abstract

Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based on item IDs. However, this kind of approach highly relies on user-item interaction data and neglects the attribute- or characteristic-level correlations among similar items preferred by a user. In light of these issues, we propose IDA-SR, which stands for ID-Agnostic User Behavior Pre-training approach for Sequential Recommendation. Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information. To bridge the gap between text semantics and sequential user behaviors, we utilize the pre-trained language model as text encoder, and conduct a pre-training architecture on the sequential user behaviors. In this way, item text can be directly utilized for sequential recommendation without relying on item IDs. Extensive experiments show that the proposed approach can achieve comparable results when only using ID-agnostic item representations, and performs better than baselines by a large margin when fine-tuned with ID information.
id不可知用户行为序列推荐预训练
近年来,顺序推荐已成为一个被广泛研究的话题。现有的研究主要是基于物品id设计有效的神经结构来模拟用户行为序列。然而,这种方法高度依赖于用户-物品交互数据,而忽略了用户偏好的类似物品之间的属性或特征级相关性。针对这些问题,我们提出了IDA-SR,即ID-Agnostic User Behavior Pre-training approach for Sequential Recommendation。IDA-SR没有显式地学习项目id的表示,而是直接从富文本信息中学习项目表示。为了弥合文本语义和顺序用户行为之间的差距,我们利用预训练的语言模型作为文本编码器,并对顺序用户行为进行预训练架构。这样,就可以直接利用项目文本进行顺序推荐,而不依赖于项目id。大量的实验表明,当只使用ID不可知的项目表示时,该方法可以获得相当的结果,并且当使用ID信息进行微调时,该方法的性能比基线要好得多。
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