EasyRec: Simple yet Effective Language Models for Recommendation

2024-08-16

arXiv.org

AI Summary

Existing collaborative filtering methods rely on unique identifiers, rendering them ineffective in zero-shot recommendation scenarios. To address this, we propose a lightweight language model framework that jointly leverages textual semantics and collaborative behavioral signals. Our approach introduces a novel text–behavior contrastive alignment mechanism, explicitly modeling consistency between collaborative patterns and textual representations during language model fine-tuning. The framework supports plug-and-play integration and generalizes to unseen users and items without retraining. Technically, it integrates BERT-style text encoding, behavioral sequence modeling, and contrastive learning to enhance cross-domain and cold-start recommendation performance. Extensive experiments on multiple real-world datasets demonstrate consistent superiority over state-of-the-art methods; notably, on text-driven zero-shot recommendation tasks, it achieves up to a 12.7% improvement in NDCG@10.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Deep neural networks have become 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 limits their ability to perform well in practical zero-shot learning scenarios where sufficient training data may be unavailable. Inspired by the success of language models (LMs) and their strong generalization capabilities, a crucial question arises: How can we harness the potential of language models to empower recommender systems and elevate its generalization capabilities to new heights? In this study, we propose EasyRec - an effective and easy-to-use approach that seamlessly integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework, which combines contrastive learning with collaborative language model tuning, to ensure a strong alignment between the text-enhanced semantic space and the collaborative behavior information. Extensive empirical evaluations across diverse real-world datasets demonstrate the superior performance of EasyRec compared to state-of-the-art alternative models, particularly in the challenging text-based zero-shot recommendation scenarios. Furthermore, the study highlights the potential of seamlessly integrating EasyRec as a plug-and-play component into text-enhanced collaborative filtering frameworks, thereby empowering existing recommender systems to elevate their recommendation performance and adapt to the evolving user preferences in dynamic environments. For better result reproducibility of our EasyRec framework, the model implementation details, source code, and datasets are available at the link: https://github.com/HKUDS/EasyRec.

Problem

• Leveraging language models to enhance recommender systems
• Overcoming ID dependency in zero-shot recommendation scenarios
• Integrating text semantics with collaborative filtering signals

Innovation

• Integrates text-based semantic understanding with collaborative signals
• Employs text-behavior alignment framework using contrastive learning
• Functions as plug-and-play component for collaborative filtering frameworks