EasyRec: Simple yet Effective Language Models for Recommendation

• URL: http://arxiv.org/abs/2408.08821v4
• Date: Sat, 18 Oct 2025 13:29:43 GMT
• Title: EasyRec: Simple yet Effective Language Models for Recommendation
• Authors: Xubin Ren, Chao Huang,
• Abstract summary: EasyRec is an effective approach that integrates text-based semantic understanding with collaborative signals.<n>It significantly outperforms state-of-the-art models, particularly in text-based zero-shot recommendation.<n>EasyRec functions as a plug-and-play component that integrates seamlessly into collaborative filtering frameworks.
• Score: 9.133822494403786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have emerged as a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. However, many existing methods heavily rely on unique user and item IDs, which restricts their performance in zero-shot learning scenarios. Inspired by the success of language models (LMs) and their robust generalization capabilities, we pose the question: How can we leverage language models to enhance recommender systems? We propose EasyRec, an effective approach that integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework that combines contrastive learning with collaborative language model tuning. This ensures strong alignment between text-enhanced semantic representations and collaborative behavior information. Extensive evaluations across diverse datasets show EasyRec significantly outperforms state-of-the-art models, particularly in text-based zero-shot recommendation. EasyRec functions as a plug-and-play component that integrates seamlessly into collaborative filtering frameworks. This empowers existing systems with improved performance and adaptability to user preferences. Implementation codes are publicly available at: https://github.com/HKUDS/EasyRec.

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