mlsanityData to 7 Oct 2026

Large Language Models Make Sample-Efficient Recommender Systems

arXiv 2406.02368 ↗First posted 4 Jun 2024cs.IR cs.CLcited by 3

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Abstract (arXiv)

Large language models (LLMs) have achieved remarkable progress in the field of natural language processing (NLP), demonstrating remarkable abilities in producing text that resembles human language for various tasks. This opens up new opportunities for employing them in recommender systems (RSs). In this paper, we specifically examine the sample efficiency of LLM-enhanced recommender systems, which pertains to the model's capacity to attain superior performance with a limited quantity of training data. Conventional recommendation models (CRMs) often need a large amount of training data because of the sparsity of features and interactions. Hence, we propose and verify our core viewpoint: Large Language Models Make Sample-Efficient Recommender Systems. We propose a simple yet effective framework (i.e., Laser) to validate the viewpoint from two aspects: (1) LLMs themselves are sample-efficient recommenders; and (2) LLMs, as feature generators and encoders, make CRMs more sample-efficient. Extensive experiments on two public datasets show that Laser requires only a small fraction of training samples to match or even surpass CRMs that are trained on the entire training set, demonstrating superior sample efficiency.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 52 of its 62 references that are in the map. First posted on arXiv 4 Jun 2024.

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52 of 62 references.

References · 52

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38 more
  1. 2023Text Is All You Need: Learning Language Representations for Sequential Recommendationcited by 149
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  3. 2023Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisitedcited by 124
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Large Language Model Enhanced Recommender Systems: A Surveycited by 10
  2. 2025From Data to Knowledge: Evaluating How Efficiently Language Models Learn Factscited by 1
  3. 2026Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendationscited by 0