Probing the LLMs for Generating User Profile of Session-based Social Recommendation

Abstract

Session-based social recommendation aims to enhance session-based recommendation by incorporating social network information, making it particularly useful in cold-start scenarios. Existing SSR methods predominantly rely on graph neural networks to model social relationships, which often introduces computational inefficiencies and fails to fully leverage rich semantic and behavioral information. In this paper, we propose UPRec, a user profile-enhanced SSR framework that integrates large language models to refine user representations from three complementary perspectives: (1) text-based history embedding, which extracts semantic features from users’ historical interactions using pre-trained LLMs; (2) prompt-guided LLM embedding, which generates dynamic user interest representations through task-specific prompts; and (3) similarity-based social embedding, which captures behavioral similarities in user-item interactions and social relationships. By leveraging LLMs for user profiling and employing cosine similarity to preprocess social relationships, UPRec eliminates the need for complex GNN-based modeling while preserving efficiency and effectiveness in session-based recommendations. Extensive experiments and ablation studies on benchmark datasets demonstrate that UPRec outperforms state-of-the-art methods in both recommendation accuracy and computational efficiency.