LLMRec: Large Language Models with Graph Augmentation for Recommendation
(2311.00423)Abstract
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in LLMs, which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.git
Overview
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LLMRec introduces a new framework that uses LLMs for graph augmentation in recommender systems, enhancing user-item interactions.
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The methodology involves reinforcing user-item interaction edges, enhancing item node attributes, and profiling user nodes through LLMs to improve recommendation accuracy.
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Theoretical analysis shows that leveraging LLMs addresses data sparsity and low-quality side information, advancing the state-of-the-art in recommendation systems.
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Future directions include integrating causal inference and adapting to dynamic user preferences for more personalized and accurate recommendations.
LLMRec: Enhancing Recommender Systems with LLMs and Graph Augmentation
Introduction
Recommender systems are an essential part of online services, helping users navigate through vast amounts of content by suggesting items of interest. Traditional methods have focused on analysing user-item interaction patterns, often extending their capabilities by incorporating side information to improve recommendation quality. However, these methods face significant challenges, including data sparsity and the quality of the side information used. To address these issues, we introduce a novel framework, LLMRec, which utilizes LLMs for graph augmentation in recommendation systems. This approach aims to tackle the limitations of sparse implicit feedback and low-quality auxiliary information by enhancing the interaction graph from a natural language perspective.
Methodology
The core of the LLMRec framework is to augment the recommendation process through three strategies:
- Reinforcing User-Item Interaction Edges: LLMs are employed to sample pair-wise training data, augmenting potential interactions based on the textual content, thus increasing effective supervision signals.
- Enhancing Item Node Attributes: We generate additional attributes for items, leveraging the deep knowledge embedded in LLMs to improve the descriptiveness and relevancy of item features.
- Conducting User Node Profiling: By analyzing textual content related to user interactions, LLMs can generate enriched user profiles that better reflect individual preferences.
To maintain the quality of the augmented data, a denoised data robustification mechanism is introduced. It comprises noisy implicit feedback pruning and MAE-based feature enhancement, targeting the refinement of both augmented interactions and node attributes. These measures ensure the reliability of the LLM-generated content, preserving the fidelity of user preferences and item characteristics.
Theoretical Analysis and Practical Implications
From a theoretical standpoint, employing LLMs as augmentors addresses critical issues in recommender systems by providing a richer representation of user-item interactions and side information. Practically, the LLMRec framework significantly improves recommendation accuracy as demonstrated through extensive experiments on benchmark datasets. The framework not only contributes to advancing the state-of-the-art in recommendation systems but also opens avenues for leveraging the power of LLMs in understanding and predicting user preferences more accurately.
Future Directions
While LLMRec marks a significant step forward, several avenues remain open for further exploration. Integrating causal inference with LLM-based augmentation could offer deeper insights into user behavior, providing a robust foundation for counterfactual reasoning in recommendations. Furthermore, extending the framework to accommodate dynamic user preferences and contextual variations presents an exciting challenge, promising to enhance the personalization and adaptiveness of recommender systems.
Conclusion
In summary, the proposed LLMRec framework showcases the potential of leveraging LLMs for data augmentation in recommendation systems. By addressing the twin challenges of sparse interactions and low-quality side information, LLMRec sets a new benchmark for recommendation accuracy, reaffirming the importance of incorporating semantic understanding and contextual knowledge in modeling user-item relationships. As we look to the future, the intersection of LLMs and recommendation systems promises to yield innovative solutions tailored to the evolving landscape of user preferences and online content.
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