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

TL;DR

This paper introduces a multi-hop path-aware recommendation framework for heterogeneous information networks that models user preferences through path selection, semantic representation, and attention-based fusion, significantly improving recommendation accuracy.

Contribution

It proposes a novel multi-hop path modeling approach with path filtering, sequential encoding, and attention fusion, advancing the state-of-the-art in heterogeneous network recommendation systems.

Findings

Outperforms existing models on Amazon-Book dataset across multiple metrics.

Effectively captures high-order interaction semantics in heterogeneous networks.

Demonstrates the framework's ability to model complex user preferences.

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.