Abstract
AbstractUsing 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.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Computer Vision and Pattern Recognition,Modeling and Simulation,Signal Processing,Control and Systems Engineering