User Information Enhanced Knowledge Graph Convolutional Networks for Recommender Systems

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in...

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Bibliographic Details
Published inInternational Conference on Measuring Technology and Mechatronics Automation (Print) pp. 1232 - 1237
Main Authors Hao, Junyu, Li, Xiaoge
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2022
Subjects
Automation
Data models
Influence diffusion
Knowledge engineering
Knowledge graph
Mechatronics
Motion pictures
Recommender systems
Semantics
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Summary:To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user's information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods.
ISSN:2157-1481
DOI:10.1109/ICMTMA54903.2022.00246