Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks

2025-05-09

AI Summary

To address the limited recommendation performance caused by insufficient modeling of high-order semantic relationships in heterogeneous information networks (HINs), this paper proposes a multi-hop semantic path-aware recommendation framework. Methodologically: (1) a semantic-saliency-based path filtering mechanism is designed to efficiently retain discriminative multi-hop paths; (2) a sequential semantic modeling module jointly encodes entities and relations, compatible with LSTM or Transformer architectures; (3) a path-semantic-importance-driven dynamic multi-head attention fusion strategy is introduced. The key innovations lie in the novel path filtering paradigm, the entity-relation co-sequential modeling architecture, and the semantics-aware attention mechanism. Extensive experiments on benchmark datasets (e.g., Amazon-Book) demonstrate significant improvements over state-of-the-art methods in HR@10, Recall@10, and Precision@10, validating the effectiveness of multi-hop semantic paths for modeling users’ high-order interests.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This study focuses on the problem of path modeling in heterogeneous information networks and proposes a multi-hop path-aware recommendation framework. The method centers on multi-hop paths composed of various types of entities and relations. It models user preferences through three stages: path selection, semantic representation, and attention-based fusion. In the path selection stage, a path filtering mechanism is introduced to remove redundant and noisy information. In the representation learning stage, a sequential modeling structure is used to jointly encode entities and relations, preserving the semantic dependencies within paths. In the fusion stage, an attention mechanism assigns different weights to each path to generate a global user interest representation. Experiments conducted on real-world datasets such as Amazon-Book show that the proposed method significantly outperforms existing recommendation models across multiple evaluation metrics, including HR@10, Recall@10, and Precision@10. The results confirm the effectiveness of multi-hop paths in capturing high-order interaction semantics and demonstrate the expressive modeling capabilities of the framework in heterogeneous recommendation scenarios. This method provides both theoretical and practical value by integrating structural information modeling in heterogeneous networks with recommendation algorithm design. It offers a more expressive and flexible paradigm for learning user preferences in complex data environments.

Problem

• Modeling multi-hop paths in heterogeneous networks for recommendations
• Capturing high-order interaction semantics through path-aware framework
• Improving recommendation accuracy with attention-based path fusion

Innovation

• Multi-hop path-aware framework for recommendations
• Attention-based fusion for user interest representation
• Sequential modeling of semantic path dependencies