Large Language Models Enhanced Hyperbolic Space Recommender Systems

Abstract: LLMs have attracted significant attention in recommender systems for their excellent world knowledge capabilities. However, existing methods that rely on Euclidean space struggle to capture the rich hierarchical information inherent in textual and semantic data, which is essential for capturing user preferences. The geometric properties of hyperbolic space offer a promising solution to address this issue. Nevertheless, integrating LLMs-based methods with hyperbolic space to effectively extract and incorporate diverse hierarchical information is non-trivial. To this end, we propose a model-agnostic framework, named HyperLLM, which extracts and integrates hierarchical information from both structural and semantic perspectives. Structurally, HyperLLM uses LLMs to generate multi-level classification tags with hierarchical parent-child relationships for each item. Then, tag-item and user-item interactions are jointly learned and aligned through contrastive learning, thereby providing the model with clear hierarchical information. Semantically, HyperLLM introduces a novel meta-optimized strategy to extract hierarchical information from semantic embeddings and bridge the gap between the semantic and collaborative spaces for seamless integration. Extensive experiments show that HyperLLM significantly outperforms recommender systems based on hyperbolic space and LLMs, achieving performance improvements of over 40%. Furthermore, HyperLLM not only improves recommender performance but also enhances training stability, highlighting the critical role of hierarchical information in recommender systems.

Large Language Models Enhanced Hyperbolic Space Recommender Systems

The paper titled "Large Language Models Enhanced Hyperbolic Space Recommender Systems" introduces a novel framework named HyperLLM, which integrates structural and semantic hierarchical information into hyperbolic space recommender systems using Large Language Models (LLMs). This work addresses the challenges faced by existing recommender systems in capturing complex relationships and hierarchical structures within user-item interactions. The authors propose a comprehensive methodology to harness the capabilities of LLMs in enhancing the representation and performance of hyperbolic space models.

Methodology

The proposed framework, HyperLLM, is model-agnostic and leverages the geometric properties of hyperbolic space, which is well-suited for modeling hierarchical and complex data structures. The framework consists of three primary modules: LLMs-based Structural Extraction, Meta-optimized Semantic Extraction, and Structural and Semantic Integration.

1. LLMs-based Structural Extraction: This module utilizes LLMs to generate multi-level classification tags with hierarchical relationships for each item from the textual data. These tags represent a structural hierarchy, akin to taxonomies, that reflects broad and specific categories related to the items. Additionally, LLMs are used to create preference summaries from the textual data, which are then encoded into semantic embeddings.

2. Meta-optimized Semantic Extraction: This component bridges the gap between semantic and hyperbolic spaces. A Mixture of Experts (MoE) model is employed to transform the semantic embeddings into a form that aligns with hyperbolic collaborative space. By freezing certain parameters and employing a meta-optimized training strategy, this module extracts hierarchical information from semantic data without compromising the model’s ability to generalize.

3. Structural and Semantic Integration: The final module integrates the aforementioned structured and semantic hierarchical information into the hyperbolic space recommender system. It combines user-item and tag-item interaction matrices to enhance learning through hyperbolic graph convolution networks, while utilizing contrastive learning to align representations across different domains.

Experimental Evaluation

The authors conducted extensive experiments on publicly available datasets such as Amazon-Toys, Amazon-Sports, and Amazon-Beauty. The results demonstrate that the HyperLLM framework significantly surpasses existing hyperbolic space models and state-of-the-art LLMs-based recommender systems. Notably, HyperLLM achieved performance improvements exceeding 40% on certain baselines, showcasing its efficacy in integrating and utilizing hierarchical information. The framework also improves training stability and demonstrates the capability to address data sparsity and the long-tail problem by enhancing recommendations for users with sparse interactions.

Implications and Future Directions

The integration of LLMs with hyperbolic space models opens new avenues for enhancing recommender systems by exploiting the hierarchical and semantic richness within data. Practically, this approach could lead to more accurate and nuanced recommendations, particularly in domains where data exhibits inherent hierarchical relationships. Theoretically, the work bridges distinct research areas of language models and geometric learning, highlighting potential collaborations between them.

Looking ahead, further developments could explore the scalability of HyperLLM in large-scale production environments and its adaptability to online learning scenarios. Additionally, specializing the framework for specific domains by incorporating domain knowledge into LLM-generated hierarchies could offer tailored recommendation strategies. As LLMs continue to evolve, their integration with complex data structures like hyperbolic spaces holds promise for advancing user-centric applications and complex decision-making systems.