Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations

Recent advancements in Large Language Models (LLMs) have shown significant potential in enhancing recommender systems. However, addressing the cold-start recommendation problem, where users lack historical data, remains a considerable challenge. In this paper, we introduce KALM4Rec (Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations), a novel framework specifically designed to tackle this problem by requiring only a few input keywords from users in a practical sce- nario of cold-start user recommendations. KALM4Rec operates in two main stages: candidates retrieval and LLM-based candidates re-ranking. In the first stage, keyword-driven retrieval models are used to identify potential candidates, addressing LLMs’ limitations in processing extensive tokens and reducing the risk of generat- ing misleading information. In the second stage, we employ LLMs with various prompting strategies, including zero-shot and few- shot techniques, to re-rank these candidates by integrating multiple examples directly into the LLM prompts. Our extensive evaluation, using Yelp restaurant data from three English-speaking cities and TripAdvisor hotel data, demonstrates that KALM4Rec excels in improving recommendation quality across the two domains. The framework’s adaptability to different domains highlights its poten- tial for widespread applications. By integrating in-context instruc- tions with LLMs, KALM4Rec notably enhances the performance of cold-start recommender systems, offering a novel approach to exploring solutions in this field.

Recommended citation: Kieu, H. D., Nguyen, M. D., Nguyen, T. S., & Le, D. D. (2024). Keyword-driven retrieval-augmented large language models for cold-start user recommendations. arXiv preprint arXiv:2405.19612.
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