A Dual-Key Attention Framework for Sequential Recommendation with Side Information

Abstract

Sequential recommendation (SR) aims to predict users' future interactions based on their historical behavior. Recently, deep learningbased SR models leveraging side information have gained considerable attention. Within these systems, items can be viewed from relation-based and attribute-based perspectives. The relationbased perspective characterizes items based on implicit relationships and contextual dependencies derived from user interactions. The attribute-based perspective defines items using inherent properties, such as category or genre. However, these perspectives are inherently entangled, making separate learning challenging. To address this issue, we propose a dual-key attention framework for sequential recommendation (DK-SR), which effectively learns both relation-based and attribute-based representations. DK-SR employs an attention mechanism with dual keys: one for item-level attention, facilitating relation-based representation learning, and another for attribute-level attention, enhancing attribute-based representation. Extensive experiments on four real-world datasets demonstrate that our model outperforms six state-of-the-art SR models leveraging side information. Additionally, an ablation study validates the contribution of the dual-key mechanism.