RDRec: Rationale Distillation for LLM-based Recommendation

2024-05-17

Annual Meeting of the Association for Computational Linguistics

AI Summary

Existing LLM-based recommendation methods neglect fine-grained semantic modeling of user preferences and item characteristics, resulting in limited interpretability and suboptimal accuracy. To address this, we propose RDRec, a rationale distillation framework that pioneers the use of large-model-generated interaction rationales as supervisory signals to distill knowledge into a lightweight student model. RDRec explicitly extracts and structurally represents implicit user preferences and item attribute rationales from raw user reviews. It further integrates rationale-augmented sequential modeling with Top-N recommendation. Extensive experiments on multiple public benchmarks demonstrate that RDRec achieves state-of-the-art performance, improving Recall@20 by up to 12.3% over strong baselines. The method significantly enhances both recommendation accuracy and interpretability while maintaining strong generalization capability and deployment efficiency.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning capability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our source code is released at https://github.com/WangXFng/RDRec.

Problem

• Large Language Models
• User Preferences
• Item Characteristics

Innovation

• RDRec
• Large Language Models
• Enhanced Recommendation Accuracy