Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation

Xiao  Sha; Jianwen  Wang; Xiaoran  Xu; Jianchuan  Ding

doi:10.6180/jase.202603_29(3).0004

Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation

Xiao ShaThis email address is being protected from spambots. You need JavaScript enabled to view it., Jianwen Wang, Xiaoran Xu, and Jianchuan Ding

Department of Computer Science, Hebei University of Water Resources and Electric Engineering, Cangzhou, 061001, China

Received: March 3, 2025
Accepted: May 20, 2025
Publication Date: June 15, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(3).0004

Knowledge graphs (KGs) have demonstrated their effectiveness in providing high-quality recommendations by incorporating rich semantic relationships between entities. However, existing KG-aware recommendation methods face significant challenges in sufficiently exploiting both the structural and semantic information while maintaining computational efficiency. We propose the Interdependent-path Recurrent Embedding (IPRE) framework that addresses these limitations through novel interdependent path construction and attentive encoding. The framework automatically generates interdependent paths connecting user-item pairs, preserving both semantic relationships and topological dependencies with linear time complexity. A dedicated attentive recurrent network then encodes these paths by learning relation-aware representations and adaptively weighting different predecessors’ influence. Comprehensive experiments on three real-world datasets demonstrate IPRE’s superiority, achieving average improvements of 8.79% in Hit ratio and 9.40% in NDCG over state-of-the-art methods. The framework shows particular effectiveness in sparse data scenarios, while maintaining competitive computational efficiency. These results validate IPRE’s capability to effectively transform KG information into accurate recommendations through its innovative path modeling approach.

Keywords: Recommender Systems; Knowledge Graphs; Attention Mechanism; Collaborative Filtering

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