SRLF: An Agent-Driven Set-Wise Reflective Learning Framework for Sequential Recommendation

TL;DR

SRLF introduces a set-wise reflective learning framework for sequential recommendation, leveraging LLMs to holistically assess item sets and better understand user preferences, leading to improved recommendation accuracy.

Contribution

The paper presents a novel set-wise assessment framework that enhances user preference modeling by analyzing item interrelationships, surpassing traditional point-wise methods.

Findings

Achieves state-of-the-art performance in sequential recommendation tasks.

Effectively captures complex item interrelationships and user preferences.

Demonstrates the importance of set-level analysis over point-wise approaches.

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.