Review-Based Hyperbolic Cross-Domain Recommendation

2024-03-29

AI Summary

To address performance degradation in cross-domain recommendation caused by data sparsity, this paper proposes a hyperbolic space-based, review-driven cross-domain recommendation model. Methodologically, it is the first to integrate hyperbolic geometry into a review-augmented cross-domain framework, employing hierarchy-aware textual semantic encoding and degree-normalized feature extraction to explicitly model hierarchical user–item relationships; it further introduces a structure-preserving domain alignment mechanism to mitigate embedding collapse induced by distance distortion in hyperbolic space. Extensive experiments on multiple cross-domain benchmark datasets demonstrate that the proposed model significantly outperforms Euclidean-space baselines, achieving superior recommendation accuracy, robustness, and scalability. These results validate the effectiveness of jointly leveraging hyperbolic representation learning and review-informed semantics for cross-domain recommendation.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target), has received notable attention. Nevertheless, the majority of existing methodologies assume a Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex interactions between users and items. This paper advocates a hyperbolic CDR approach based on review texts for modeling user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. The process involves the initial embedding of review texts in hyperbolic space, followed by feature extraction incorporating degree-based normalization and structure alignment. We conducted extensive experiments to substantiate the efficiency, robustness, and scalability of our proposed model in comparison to state-of-the-art baselines.

Problem

• Data Sparsity
• Cross-domain Recommendation
• Textual Review Utilization

Innovation

• Hyperbolic Space
• Review-based Recommendation
• Structural Alignment