Title: Personalised recommendation algorithm of network resources based on collaborative knowledge map

Authors: Xiaofei Liu; Shaohui Zhong

Addresses: School of Artificial Intelligence, The Open University of Guangdong, GuangZhou 510091, China ' Information College, Hunan Radio and Television University, Changsha 410078, China

Abstract: In order to overcome the problems of wide coverage of network resources and confusion of semantic information, resulting in low precision of recommendation and low recall rate, a personalised recommendation algorithm based on collaborative knowledge map is proposed. The collaborative knowledge map is introduced into the research field of network resources, the knowledge features of network resources are extracted specifically. The collaborative knowledge ontology database of network resources field is constructed according to the entity division relationship in the research field. Entities and relationships are extracted from network resources, to build a knowledge map based on relational database, quantify the network resources in the knowledge map, extract semantic information of network resources with collaborative filtering algorithm, so as to achieve the purpose of personalised recommendation of network resources. The simulation results show that the recommended precision is more than 98%, and the recall rate and comprehensive evaluation index are better.

Keywords: collaborative knowledge map; network resources; special extraction; collaborative filtering algorithm; personalised recommendation; relational database; comprehensive evaluation index.

DOI: 10.1504/IJAACS.2023.131079

International Journal of Autonomous and Adaptive Communications Systems, 2023 Vol.16 No.2, pp.159 - 174

Received: 09 Mar 2020
Accepted: 18 Jul 2020

Published online: 24 May 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article