EasyRec: Simple yet Effective Language Models for Recommendation
This addresses the limitation of existing recommender systems that rely on unique IDs, enhancing adaptability for users in zero-shot settings, though it is an incremental advancement building on language model integration.
The paper tackles the problem of zero-shot learning in recommender systems by proposing EasyRec, a method that integrates language models with collaborative filtering, achieving significant performance improvements over state-of-the-art models, especially in text-based zero-shot scenarios.
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.