LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation

• URL: http://arxiv.org/abs/2503.01814v1
• Date: Mon, 03 Mar 2025 18:41:59 GMT
• Title: LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
• Authors: Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu,
• Abstract summary: Collaborative filtering models have shown strong performance in capturing user-item interactions for recommendation systems.<n>The emergence of large language models (LLMs) like GPT and LLaMA presents new possibilities for enhancing recommendation performance.
• Score: 34.227734210743904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative filtering models, particularly graph-based approaches, have demonstrated strong performance in capturing user-item interactions for recommendation systems. However, they continue to struggle in cold-start and data-sparse scenarios. The emergence of large language models (LLMs) like GPT and LLaMA presents new possibilities for enhancing recommendation performance, especially in cold-start settings. Despite their promise, LLMs pose challenges related to scalability and efficiency due to their high computational demands and limited ability to model complex user-item relationships effectively. In this work, we introduce a novel perspective on leveraging LLMs for CF model initialization. Through experiments, we uncover an embedding collapse issue when scaling CF models to larger embedding dimensions. To effectively harness large-scale LLM embeddings, we propose innovative selective initialization strategies utilizing random, uniform, and variance-based index sampling. Our comprehensive evaluation on multiple real-world datasets demonstrates significant performance gains across various CF models while maintaining a lower computational cost compared to existing LLM-based recommendation approaches.

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