EasyRec: Simple yet Effective Language Models for Recommendation

Understand

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.