TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

Abstract

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems; September 2023 Pages; 1007-1014

References in corpus (5)

• Training language models to follow instructions with human feedback
• A Survey of Large Language Models
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
• Multi-level Contrastive Learning Framework for Sequential Recommendation
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

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• Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
• Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
• Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
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• Recommendation Unlearning via Influence Function
• Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
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• Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems
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• EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
• Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M
• Unleashing the Power of Large Language Model for Denoising Recommendation
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• ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
• Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation
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• Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
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• LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding
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• DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
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• M-$LL{M}^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
• MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender