Event-Participant and Incremental Planning over Event-Based Social Networks

In recent years, online Event Based Social Network (EBSN) platforms have become increasingly popular. One typical task of EBSN platforms is to help users make suitable and personalized plans for participating in different interesting social events. Existing techniques either ignore the minimum-parti...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 33; no. 2; pp. 474 - 488
Main Authors: Cheng, Yurong, Yuan, Ye, Chen, Lei, Giraud-Carrier, Christophe, Wang, Guoren, Li, Boyang
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
approximate algorithm
Approximation algorithms
Computer science
event-based social networks
Games
incremental planning
Planning
Platforms
Social networking (online)
Social networks
Sports
Upper bound
ISSN:1041-4347, 1558-2191
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, online Event Based Social Network (EBSN) platforms have become increasingly popular. One typical task of EBSN platforms is to help users make suitable and personalized plans for participating in different interesting social events. Existing techniques either ignore the minimum-participant requirement constraint for each event, which is crucially needed for some events to be held successfully, or assume that events would not change once announced. In this paper, we address the above inadequacies of existing EBSN techniques. We formally define the Global Event Planning with Constraints (GEPC) problem, and its incremental variant. Since these problems are NP-hard, and provide approximate solutions. Finally, we verify the effectiveness and efficiency of our proposed algorithms through extensive experiments over real and synthetic datasets.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2019.2931906