Large language models make sample-efficient recommender systems
摘要
This letter investigates the sample efficiency property of recommender systems enhanced by large language models. We propose a simple yet effective framework (i.e., Laser) to validate the core viewpoint - large language models make sample-efficient recommender systems - from two aspects: (1) LLMs themselves are sample-efficient recommenders; and (2) LLMs make conventional recommender systems more sample-efficient. For future work, we aim to improve the sample efficiency of LLM-based recommender systems from the following two aspects: (1) exploring effective strategy to select the few-shot training samples instead of uniformly sampling, and (2) applying Laser for downstream applications like code snippet recommendation.