Research Article
A New User Recommendation Model Within the Context of the Covid-19 Pandemic
@INPROCEEDINGS{10.1007/978-3-030-77424-0_21, author={Thanh Trinh}, title={A New User Recommendation Model Within the Context of the Covid-19 Pandemic}, proceedings={Industrial Networks and Intelligent Systems. 7th EAI International Conference, INISCOM 2021, Hanoi, Vietnam, April 22-23, 2021, Proceedings}, proceedings_a={INISCOM}, year={2021}, month={5}, keywords={Recommendation Covid-19 EBSNs}, doi={10.1007/978-3-030-77424-0_21} }- Thanh Trinh
Year: 2021
A New User Recommendation Model Within the Context of the Covid-19 Pandemic
INISCOM
Springer
DOI: 10.1007/978-3-030-77424-0_21
Abstract
Event-based social networks provide people with fantastic platforms to improve their relationships and make friends through offline and online activities. Predicting the event attendance of users is a challenging problem and solved by many techniques. Recently, the outbreak of Covid-19 changes the ways that users participate in events, from offline to online. In this paper, we study the problem of user recommendation within the context of the Covid-19 pandemic. To address this problem, we first analyze the information of events to obtain three factors, i.e., content, time, and location. Then, we propose a new recommendation model to compute scores of new events with respect to participated events of each user. Finally, the topNevents with the highest scores are recommended to the user. Extensive experiments were conducted on a real Meetup event dataset, and the results have shown that our model outperforms comparison methods.
