RecLM: Recommendation Instruction Tuning

• URL: http://arxiv.org/abs/2412.19302v2
• Date: Wed, 01 Jan 2025 13:49:02 GMT
• Title: RecLM: Recommendation Instruction Tuning
• Authors: Yangqin Jiang, Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang,
• Abstract summary: We propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering.<n>Our proposed $underlineRec$ommendation enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function.
• Score: 17.780484832381994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Networks (GNNs) excel at capturing user-item relationships, their effectiveness is limited when handling sparse data or zero-shot scenarios, primarily due to constraints in ID-based embedding functions. To address these challenges, we propose a model-agnostic recommendation instruction-tuning paradigm that seamlessly integrates large language models with collaborative filtering. Our proposed $\underline{Rec}$ommendation $\underline{L}$anguage $\underline{M}$odel (RecLM) enhances the capture of user preference diversity through a carefully designed reinforcement learning reward function that facilitates self-augmentation of language models. Comprehensive evaluations demonstrate significant advantages of our approach across various settings, and its plug-and-play compatibility with state-of-the-art recommender systems results in notable performance enhancements. The implementation of our RecLM framework is publicly available at: https://github.com/HKUDS/RecLM.

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