Research on Lightweight LLM Recommendation Algorithm in Few-Shot Cold-Start Scenarios

Zhenyu Ni

doi:10.71222/4rktf787

Authors

Zhenyu Ni Jushenggao (Shanghai) Digital Technology Co., Ltd, Shanghai, China Author

DOI:

https://doi.org/10.71222/4rktf787

Keywords:

recommendation systems, cold start, few-shot learning, language models, model fine-tuning, knowledge distillation

Abstract

To address the persistent challenges of feature sparsity, weak generalization ability, and high computational cost faced by traditional recommendation systems in few-shot cold-start scenarios, this paper proposes a novel, lightweight large language model (LLM)-based recommendation algorithm named LLM-RecLite. As digital platforms increasingly rely on personalized content delivery, mitigating the cold-start problem remains critical for user retention. The proposed LLM-RecLite algorithm first performs rigorous domain adaptation on lightweight LLMs using parameter-efficient fine-tuning techniques, specifically QLoRA. This step effectively bridges the semantic gap between general-purpose linguistic representations and specific recommendation tasks without incurring prohibitive training costs. Secondly, the methodology incorporates a meticulously designed hierarchical prompt template that seamlessly integrates historical user-item interactions with rich content features, enabling robust semantic reasoning under strictly few-shot conditions. Finally, the framework introduces an advanced knowledge distillation mechanism to transfer the complex reasoning capabilities of the larger model to a significantly more lightweight inference model. This ensures the system meets the stringent low-latency performance requirements of real-time recommendation environments. Comprehensive experimental results conducted on two widely recognized public datasets, MovieLens-1M and Amazon Beauty, demonstrate the superior efficacy of the proposed approach. Compared with traditional cold-start algorithms and mainstream LLM-based recommendation frameworks, LLM-RecLite significantly improves the NDCG@10 metric by 18.3% and 9.7%, respectively, while simultaneously increasing inference speed by 4.2 times. Ultimately, this research effectively balances recommendation accuracy and computational efficiency, providing a highly feasible and scalable solution for few-shot cold-start recommendations in resource-constrained industrial applications.

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