Hybrid Graph Neural Network Recommendation Based on Multi-Behavior Interaction and Time Sequence Awareness

Author:

Jia Mingyu1ORCID,Liu Fang’ai1,Li Xinmeng1,Zhuang Xuqiang1

Affiliation:

1. School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Abstract

In recent years, mining user multi-behavior information for prediction has become a hot topic in recommendation systems. Usually, researchers only use graph networks to capture the relationship between multiple types of user-interaction information and target items, while ignoring the order of interactions. This makes multi-behavior information underutilized. In response to the above problem, we propose a new hybrid graph network recommendation model called the User Multi-Behavior Graph Network (UMBGN). The model uses a joint learning mechanism to integrate user–item multi-behavior interaction sequences. We designed a user multi-behavior information-aware layer to focus on the long-term multi-behavior features of users and learn temporally ordered user–item interaction information through BiGRU units and AUGRU units. Furthermore, we also defined the propagation weights between the user–item interaction graph and the item–item relationship graph according to user behavior preferences to capture more valuable dependencies. Extensive experiments on three public datasets, namely MovieLens, Yelp2018, and Online Mall, show that our model outperforms the best baselines by 2.04%, 3.82%, and 3.23%.

Funder

the National Natural Science Foundation of Shandong

the National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3