Architecture is All You Need: Improving LLM Recommenders by Dropping the Text

• URL: http://arxiv.org/abs/2506.15833v1
• Date: Wed, 18 Jun 2025 19:18:49 GMT
• Title: Architecture is All You Need: Improving LLM Recommenders by Dropping the Text
• Authors: Kevin Foley, Shaghayegh Agah, Kavya Priyanka Kakinada,
• Abstract summary: We propose a recommender model that uses the architecture of large language models (LLMs) while reducing layer count and dimensions.<n>We find that this simplified approach substantially outperforms both traditional sequential recommender models and PLM-based recommender models.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been an explosion of interest in the applications of large pre-trained language models (PLMs) to recommender systems, with many studies showing strong performance of PLMs on common benchmark datasets. PLM-based recommender models benefit from flexible and customizable prompting, an unlimited vocabulary of recommendable items, and general ``world knowledge'' acquired through pre-training on massive text corpora. While PLM-based recommenders show promise in settings where data is limited, they are hard to implement in practice due to their large size and computational cost. Additionally, fine-tuning PLMs to improve performance on collaborative signals may degrade the model's capacity for world knowledge and generalizability. We propose a recommender model that uses the architecture of large language models (LLMs) while reducing layer count and dimensions and replacing the text-based subword tokenization of a typical LLM with discrete tokens that uniquely represent individual content items. We find that this simplified approach substantially outperforms both traditional sequential recommender models and PLM-based recommender models at a tiny fraction of the size and computational complexity of PLM-based models. Our results suggest that the principal benefit of LLMs in recommender systems is their architecture, rather than the world knowledge acquired during extensive pre-training.

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