Efficiently Exploiting Muti-Level User Initial Intent for Session-Based Recommendation

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Session-based recommendation (SBR) aims to predict potential user interactions within an anonymous session. It utilizes learned user interests to recommend items. As research has progressed, researchers have shifted towards exploring user initial intent, which can provide practical guidance for item selection. However, a significant limitation of the current methodologies is that they often assume the first item in a session as the initial intent, neglecting the possibility of a random initial click. Additionally, these methods typically merge the initial intent with the session representation without considering dynamic changes in user interests. To address these challenges, we propose an innovative approach named Efficiently Exploiting Muti-level User Initial Intent for (EMUI) for session-based recommendation. This approach includes a multi-level initial-intent generation module (MIGM) and an interest matching module (IMM). Specifically, the MIGM is designed to extract a more comprehensive representation of user initial intent from various levels, effectively mitigating the issue of random initial clicks. Furthermore, we propose the IMM to ensure alignment between dynamic interests and user initial intent. The IMM identifies components within multi-level user initial intent that correlate with dynamic interests, thereby enhancing session representation and, ultimately, improving recommendation performance. In addition, in order to optimize the initial user intent at each level, we introduce a contrastive learning task to maximize the use of user initial intent at each level. A considerable number of experiments on three real-world datasets have shown that our EMUI has significantly enhanced the recommendation accuracy over state-of-the-art methods.