RecLM: Recommendation Instruction Tuning

TL;DR

RecLM introduces a recommendation instruction-tuning framework that integrates large language models with collaborative filtering, improving user preference understanding and performance in sparse or zero-shot scenarios.

Contribution

It presents a model-agnostic, reinforcement learning-based instruction-tuning paradigm that enhances recommender systems by combining language models with collaborative filtering.

Findings

Significant performance improvements across various settings

Effective handling of sparse data and zero-shot scenarios

Plug-and-play compatibility with existing systems

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.