Large Language Models Are Universal Recommendation Learners

• URL: http://arxiv.org/abs/2502.03041v1
• Date: Wed, 05 Feb 2025 09:56:52 GMT
• Title: Large Language Models Are Universal Recommendation Learners
• Authors: Junguang Jiang, Yanwen Huang, Bin Liu, Xiaoyu Kong, Ziru Xu, Han Zhu, Jian Xu, Bo Zheng,
• Abstract summary: Large language models (LLMs) can function as universal recommendation learners. We introduce a multimodal fusion module for item representation and a sequence-in-set-out approach for efficient candidate generation. Our analysis reveals that recommendation outcomes are highly sensitive to text input.
• Score: 27.16327640562273
• License:
• Abstract: In real-world recommender systems, different tasks are typically addressed using supervised learning on task-specific datasets with carefully designed model architectures. We demonstrate that large language models (LLMs) can function as universal recommendation learners, capable of handling multiple tasks within a unified input-output framework, eliminating the need for specialized model designs. To improve the recommendation performance of LLMs, we introduce a multimodal fusion module for item representation and a sequence-in-set-out approach for efficient candidate generation. When applied to industrial-scale data, our LLM achieves competitive results with expert models elaborately designed for different recommendation tasks. Furthermore, our analysis reveals that recommendation outcomes are highly sensitive to text input, highlighting the potential of prompt engineering in optimizing industrial-scale recommender systems.

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