Context-Aware Recommendation System using Graph-based Behaviours Analysis  

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

机构地区：[1]Auckland University of Technology,Auckland,New Zealand [2]University of Tasmania,Hobart,Australia

出　　处：《Journal of Systems Science and Systems Engineering》2021年第4期482-494,共13页系统科学与系统工程学报（英文版）

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.

关 键 词：Contextual information extraction knowledge graph CONTEXT-AWARENESS recommendation system user behaviour analysis 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]