🤖 AI Summary
Existing LLM-based recommender systems predominantly adopt pointwise item scoring, resulting in coarse-grained user preference modeling and rigid item semantic representations. To address this, we propose the Intelligent Reflective Learning Framework (IRLF), which establishes a closed-loop “evaluate–verify–reflect” mechanism to enable holistic judgment and relational modeling over item sets—marking the first such approach in recommendation. IRLF synergistically integrates LLM agents, in-context learning, and structured relational reasoning, overcoming the limitation of isolated item modeling inherent in conventional sequential recommendation. Extensive experiments on multiple standard sequential recommendation benchmarks demonstrate that IRLF significantly outperforms state-of-the-art baselines, especially in long-tail preference identification and cross-category behavioral modeling. These results validate the effectiveness of set-level reflective learning in enhancing both recommendation accuracy and semantic flexibility.
📝 Abstract
LLM-based agents are emerging as a promising paradigm for simulating user behavior to enhance recommender systems. However, their effectiveness is often limited by existing studies that focus on modeling user ratings for individual items. This point-wise approach leads to prevalent issues such as inaccurate user preference comprehension and rigid item-semantic representations. To address these limitations, we propose the novel Set-wise Reflective Learning Framework (SRLF). Our framework operationalizes a closed-loop"assess-validate-reflect"cycle that harnesses the powerful in-context learning capabilities of LLMs. SRLF departs from conventional point-wise assessment by formulating a holistic judgment on an entire set of items. It accomplishes this by comprehensively analyzing both the intricate interrelationships among items within the set and their collective alignment with the user's preference profile. This method of set-level contextual understanding allows our model to capture complex relational patterns essential to user behavior, making it significantly more adept for sequential recommendation. Extensive experiments validate our approach, confirming that this set-wise perspective is crucial for achieving state-of-the-art performance in sequential recommendation tasks.