LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation (2401.08217v2)

Abstract: As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests. To enable more human-centric modeling of user preferences, this work proposes a novel explainable recommendation framework, i.e., LLMHG, synergizing the reasoning capabilities of LLMs and the structural advantages of hypergraph neural networks. By effectively profiling and interpreting the nuances of individual user interests, our framework pioneers enhancements to recommendation systems with increased explainability. We validate that explicitly accounting for the intricacies of human preferences allows our human-centric and explainable LLMHG approach to consistently outperform conventional models across diverse real-world datasets. The proposed plug-and-play enhancement framework delivers immediate gains in recommendation performance while offering a pathway to apply advanced LLMs for better capturing the complexity of human interests across machine learning applications.

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation: An Expert Summary

The manuscript titled "LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation" presents a noteworthy advancement in personalized recommendation systems by synergizing the capabilities of LLMs with hypergraph neural networks to enhance the explainability and performance of recommendation engines. The work is motivated by the inherent limitations of traditional recommendation methodologies, which often rely exclusively on users' historical interactions and overlook the complex, multifaceted nature of human interests.

Framework Overview

The proposed framework, termed LLMHG (LLM Hypergraph), addresses these limitations by integrating LLMs' semantic reasoning abilities with the expressive power of hypergraphs. This fusion enables more nuanced profiling of user preferences, thus enhancing the explainability of recommendations. In particular, LLMHG leverages LLMs to discern interest angles (IAs) from users' past interactions. These IAs constitute structured representations of user preferences, which are subsequently used to generate a hypergraph capturing the higher-order relationships among items.

The framework proceeds through several key stages:

1. Interest Angle Generation: LLMs perform an initial extraction of IAs by analyzing a user's historical behavior. This structured approach informs the categorization of items within various IAs.
2. Multi-View Hypergraph Construction: Using the systematically extracted IAs, a multi-view hypergraph is constructed. Each view corresponds to a specific angle (e.g., genre, style), and hyperedges within each view denote groupings of items sharing common attributes.
3. Hypergraph Structure Learning: Herein lies the novel integration of hypergraph neural networks. Through a process akin to structured learning, the hypergraph undergoes refinement, focusing on salient interest facets while suppressing non-informative elements. This step effectively bridges any reasoning gaps inherent in LLM-derived data, resulting in an optimized substrate for recommendation generation.
4. Representation Fusion: Finally, the refined hypergraph embeddings are merged with latent representations derived from sequential recommendation models. This integration provides a robust base for accurate downstream item prediction.

Empirical Evaluation and Implications

The framework's efficacy is validated through extensive experiments across diverse datasets, demonstrating consistent improvements over baseline recommendation models in HR@5, HR@10, NDCG@5, and NDCG@10 metrics. The integration of LLM guidance notably enhances performance, especially in datasets with rich user interaction history, underscoring the practical utility of LLMHG in addressing the challenges associated with sparse and noisy data.

The implications of this work are multifaceted. From a practical standpoint, the framework offers a plug-and-play enhancement mechanism for existing recommendation systems. By harnessing LLMs alongside hypergraph-based inference, it facilitates the development of more human-centric and interpretable recommenders that better cater to the complexities of diversified user preferences.

Future Directions

Looking forward, several areas merit exploration. First, tightly integrating the LLM and hypergraph components into a cohesive end-to-end learning paradigm could further optimize recommendations. Second, refining hypergraph learning algorithms to specifically target prevalent LLM reasoning errors may yield additional gains in both accuracy and interpretability.

Overall, this paper contributes significantly to the field of recommendation systems, demonstrating how advanced AI techniques can be orchestrated to navigate and uncover the intricacies embedded in human preferences. Further research could build on this foundation, delving deeper into the theoretical modeling of recommendation systems using sophisticated AI paradigms.