Abstract
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model’s interpretability and accuracy. This paper introduces an end-to-end deep learning model, named representation-enhanced knowledge graph convolutional networks (RKGCN), which dynamically analyses each user’s preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.
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Chen Li received the Ph. D. degree in engineering from Hiroshima University, Japan in 2019. In 2019 and 2020, he was a visiting researcher with Graduate School of Advanced Science and Engineering, Hiroshima University, Japan. Since 2021, he has been a research fellow with the Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Japan.
His research interests include deep learning, data mining, and performance evaluation, with research topics focusing on classification, prediction, and generation of time-series data using AI techniques, information retrieval from spatial data, parameter estimation and performance evaluation on queueing systems.
Yang Cao received the B. Sc. degree in information technology from Monash University, Australia in 2021, and the M.Sc. degree in data science from Deakin University, Australia in 2022. He is currently a Ph. D. degree candidate at Deakin University, Australia.
His research interests include clustering analysis, anomaly detection and their application in renewable energy.
Ye Zhu received the Ph. D. degree in artificial intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University, Australia in 2017. He is a senior lecturer at School of Information Technology, Deakin University, Australia. He has published more than 40 papers in AI-related top international conferences or journals, including SIGKDD, AAAI, IJCAI, VLDB, AIJ, TKDE, PRJ, JAIR, ISJ and MLJ. He is on the program committee of SIGKDD, AAAI, IJCAI, PAKDD and ADMA. He has also secured several large research grants for multi-disciplinary research. He is an IEEE senior member.
His research interests include clustering analysis, anomaly detection, and their applications for pattern recognition and information retrieval.
Debo Cheng received the Ph. D. degree in computer and information science from the University of South Australia (UniSA), Australia in 2021. He is currently a postdoctoral researcher with UniSA.
His research interests include data mining, machine learning, and causal inference.
Chengyuan Li received the M. Eng. degree in engineering from Hiroshima University, Japan in 2021. Since 2021, he has been an associate devops engineer of Rakuten Group Inc., Japan.
His research interests include deep learning, recommendation systems, and graph data processing.
Yasuhiko Morimoto received the B.Eng., M. Eng. and Ph. D. degrees in engineering from Hiroshima University, Japan in 1989, 1991 and 2002, respectively. He is a professor at Hiroshima University, Japan. From 1991 to 2002, he had been with IBM Tokyo Research Laboratory where he worked for data mining projects and multimedia database projects. Since 2002, he has been with Hiroshima University, Japan.
His research interests include data mining, machine learning, geographic information systems and privacy preserving information retrieval.
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Li, C., Cao, Y., Zhu, Y. et al. Ripple Knowledge Graph Convolutional Networks for Recommendation Systems. Mach. Intell. Res. 21, 481–494 (2024). https://doi.org/10.1007/s11633-023-1440-x
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DOI: https://doi.org/10.1007/s11633-023-1440-x