Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

• URL: http://arxiv.org/abs/2405.03988v1
• Date: Tue, 7 May 2024 04:00:30 GMT
• Title: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application
• Authors: Jian Jia, Yipei Wang, Yan Li, Honggang Chen, Xuehan Bai, Zhaocheng Liu, Jian Liang, Quan Chen, Han Li, Peng Jiang, Kun Gai,
• Abstract summary: Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems. We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
• Score: 54.984348122105516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items. However, this approach overlooks the wealth of semantic information embedded within textual descriptions of items, leading to suboptimal performance in cold-start scenarios and long-tail user recommendations. Leveraging the capabilities of Large Language Models (LLMs) pretrained on massive text corpus presents a promising avenue for enhancing recommender systems by integrating open-world domain knowledge. In this paper, we propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge. We address computational complexity concerns by utilizing pretrained LLMs as item encoders and freezing LLM parameters to avoid catastrophic forgetting and preserve open-world knowledge. To bridge the gap between the open-world and collaborative domains, we design a twin-tower structure supervised by the recommendation task and tailored for practical industrial application. Through offline experiments on the large-scale industrial dataset and online experiments on A/B tests, we demonstrate the efficacy of our approach.

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