Context-Aware Recommendation System using Graph-based Behaviours Analysis

作     者：Lan Zhang Xiang Li Weihua Li Huali Zhou Quan Bai Lan Zhang;Xiang Li;Weihua Li;Huali Zhou;Quan Bai

作者机构：Auckland University of TechnologyAucklandNew Zealand University of TasmaniaHobartAustralia 

出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)

年 卷 期：2021年第30卷第4期

页      面：482-494页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We would like to thank Venkata Krishnan Venkateswara for his useful discussions and proofreading of this paper. We would also like to thank the referees for carefully reading our manuscript and giving such constructive comments that substantially helped improve the quality of the paper 

主　　题：Contextual information extraction knowledge graph context-awareness recommendation system user behaviour analysis 

摘      要：Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.