EasyRec: Simple yet Effective Language Models for Recommendation (2408.08821v3)

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 LLMs (LMs) and their strong generalization capabilities, a crucial question arises: How can we harness the potential of LLMs 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 LLM 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.

EasyRec: Simple yet Effective LLMs for Recommendation

The research paper titled "EasyRec: Simple yet Effective LLMs for Recommendation" investigates the integration of LLMs (LMs) with collaborative filtering (CF) techniques to enhance recommender systems. The authors propose a novel framework, EasyRec, designed to merge the semantic understanding capabilities of text-based LMs with the collaborative signals inherent in user-item interactions.

Key Contributions

The paper makes several significant contributions to the field of recommender systems:

1. Motivation: The primary goal is to introduce a recommender system operating as a zero-shot learner with robust generalization capabilities. This system, constructed upon LLMs, is capable of adapting seamlessly to new recommendation data.
2. Methodology: EasyRec introduces a contrastive learning-powered collaborative LLMing approach. This method aligns text-based semantic encoding with collaborative signals from user behavior to enhance the model's ability to capture both semantic representations and underlying behavioral patterns.
3. Zero-Shot Recommendation Capability: Through rigorous experiments, the EasyRec framework demonstrates substantial advantages over baseline methods in terms of recommendation accuracy and generalization capabilities in text-based zero-shot recommendation scenarios.
4. Enhancement of Existing CF Models: EasyRec is designed as a lightweight, modular component that can be easily integrated into state-of-the-art collaborative filtering models. This enhances their performance significantly, particularly in dynamically evolving environments.

Methodological Details

Collaborative User and Item Profiling:

The generation of user and item profiles leverages both semantic and collaborative information. LLMs such as GPT and LLaMA are used to generate textual profiles, which encompass the detailed characteristics and preferences of the entities. For items, profile generation considers components like titles and descriptions, along with user reviews. For users, the generated profiles aggregate feedback from interacted items.

Collaborative LM with Contrastive Learning:

To optimize the alignment between text-based profiles and collaborative signals, EasyRec employs a supervised contrastive loss mechanism. This approach contrasts interacted user-item pairs (positive views) and non-interacted pairs (negative views), adjusting the encoded feature space to capture high-order relationships and dependencies among users and items.

Profile Diversification for Robustness:

To enhance model generalization, EasyRec employs LLM-based profile diversification, generating multiple semantically similar but varied text profiles for each user and item. This augmentation technique enriches the training dataset by introducing controlled variations while preserving the core semantic content.

Experimental Evaluation

Datasets:

The empirical evaluations utilize datasets from diverse domains like Arts, Movies, Games, Electronics, etc., for training, and Sports, Steam, and Yelp for testing. This diversity ensures comprehensive assessment across different platforms and domains.

Performance Metrics:

EasyRec's performance is measured using Recall@$N$ and NDCG@$N$ metrics across different datasets. The results consistently show EasyRec outperforming other models in zero-shot recommendation scenarios, highlighting its strong generalization ability.

Integration with CF Models:

When integrated with collaborative filtering models like GCCF and LightGCN, EasyRec further enhances their recommendation quality. This integration underscores the practical utility of EasyRec as a plug-and-play enhancement for existing CF frameworks.

Impact of Training Objectives:

The research also explores the impact of different training objectives, demonstrating that contrastive learning significantly improves the model's ability to incorporate collaborative information compared to traditional BPR loss.

Implications and Future Directions

The implications of this research extend both practically and theoretically:

Practical Impact:

EasyRec provides a robust framework for deploying recommendation systems that adapt dynamically to new and evolving user preferences. Its modular design facilitates easy integration with existing systems, enhancing their recommendation performance even in sparse data conditions.

Theoretical Impact:

The integration of contrastive learning with LLMs presents a novel approach to capturing high-order collaborative signals within semantic text embeddings. This method opens new avenues for future research in leveraging LMs for various recommendation tasks beyond text-based recommendations.

Future Developments:

Future research could explore the integration of multi-modal data, including visual and audio content, to further enrich the user and item profiles. Additionally, enhancing the scalability and efficiency of EasyRec for real-time recommendation scenarios would be a valuable direction for practical deployment in industry settings.

Conclusion

The EasyRec framework sets a new benchmark in the development of recommendation systems that leverage the synergy between LLMs and collaborative filtering. By addressing data sparsity and offering robust zero-shot learning capabilities, EasyRec demonstrates significant potential for both academic research and real-world applications. Its effective methodology and modular design provide a promising foundation for future advancements in AI-driven recommendation technologies.