STAR: A Simple Training-free Approach for Recommendations using Large Language Models

ACL ARR 2025 February Submission558 Authors

Abstract: Recent progress in large language models (LLMs) offers promising new approaches for recommendation system tasks. While the current state-of-the-art methods rely on fine-tuning LLMs to achieve optimal results, this process is costly and introduces significant engineering complexities. Conversely, methods that directly use LLMs without additional fine-tuning result in a large drop in recommendation quality, often due to the inability to capture collaborative information. In this paper, we propose a **S**imple **T**raining-free **A**pproach for **R**ecommendation (**STAR**), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning, while maintaining high-quality recommendation performance. Our approach involves a retrieval stage that uses semantic embeddings from LLMs combined with collaborative user information to retrieve candidate items. We then apply an LLM for pairwise ranking to enhance next-item prediction. Experimental results on the Amazon Review dataset show competitive performance for next-item prediction, even with our retrieval stage alone. Our full method achieves Hits@10 performance of **+23.8%** on *Beauty*, **+37.5%** on *Toys & Games*, and **-1.8%** on *Sports & Outdoors* relative to the best supervised models. This framework offers an effective alternative to traditional supervised models, highlighting the potential of LLMs in recommendation systems without extensive training or custom architectures.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Information Retrieval, Financial/Business NLP

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 558