Meta-path guided heterogeneous graph neural networks for news recommendation

被引：0
|
作者：
Wang F. [1 ,2 ]
Lin Z. [2 ]
Wu K. [3 ]
Han S. [2 ]
Sun L. [2 ]
Lü X. [1 ,2 ]
机构：
[1] Center of Applied Statistics, Renmin University of China, Beijing
[2] School of Statistics, Renmin University of China, Beijing
[3] Recommendation Group, Bytedance, Beijing
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2024年 / 44卷 / 05期
基金：
中国国家自然科学基金;
关键词：
attention; heterogeneous graph neural networks; meta-path; news recommendation;
D O I：
10.12011/SETP2023-0313
中图分类号：
学科分类号：
摘要：
News recommendation is an important recommendation scenario, and its effectiveness relies on the thorough exploration of news textual information. In recent years, graph neural networks (GNNs) have gained widespread attention in the field of recommendation due to their powerful ability to mine higher-order information. However, there is limited research on the use of heterogeneous graph neural networks in the field of news recommendation, and existing heterogeneous graph recommendation models also suffer from the problem of information loss. In order to fully exploit the high-level information among news, users, textual topics, entities, and categories in the news recommendation scenario, we propose a meta-path guided neighbors interaction recommendation model (MPNRec) for news recommendation. The MPNRec model builds a heterogeneous graph with more types of nodes and edges fully mine high-level information and improve the performance of news recommendation. On two public datasets (i.e., MIND small and Adressa 1week), the MPNRec model can reach at least a 2% to 5% improvement in recommendation accuracy when compared with state-of-the-art methods. © 2024 Systems Engineering Society of China. All rights reserved.
引用
收藏
页码：1561 / 1576
页数：15
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