International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 618 - 633

Multi-feature based event recommendation in Event-Based Social Network*

Authors
Jiuxin Cao, †, jx.cao@seu.edu.cn, Ziqing Zhu, zzqxztc@seu.edu.cn, Liang Shi, shiliangdn@seu.edu.cn, Bo Liu, bliu@seu.edu.cn, Zhuo Ma, mazhuo@seu.edu.cn
School of Computer Science and Engineering, Southeast University, Nanjing, 211189, Jiangsu, China
Key Laboratory of Computer Network and Information Integration, Ministry of Education, Nanjing, 211189, Jiangsu, China
*

First and second authors contributed equally to this paper

†Corresponding author
Corresponding Author
Received 24 April 2017, Accepted 5 January 2018, Available Online 22 January 2018.
DOI
10.2991/ijcis.11.1.48How to use a DOI?
Keywords
Event-Based Social Network; feature analysis; scoring model; event recommendation
Abstract

As a new type of heterogeneous social network, Event-Based Social Network (EBSN) has experienced rapid development after its appearance. In EBSN, the interaction data between users and events is relatively sparse because of the short life cycle of events, which brings great challenges to event recommendation. In this paper, a multiple features based event recommendation method is proposed, which makes full use of various information in the network to mine users’ preference for event recommendation. Firstly, a heterogeneous information network model is constructed based on the intrinsic structure characteristics. Then multiple features about topology, temporal, spatial and semantic are extracted to measure the user’s event preference, and a linear scoring model is designed to acquire user’s preference score on events. At last, the bayesian personalized ranking method is used to learn the feature weights by using user-event pairs in scoring model and events are recommended to users according to the descending score order. Experiments are carried out on two real EBSN data sets, the results show that our approach can effectively alleviate the data sparseness problem and achieve better recommendation results.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
618 - 633
Publication Date
2018/01/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.48How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Jiuxin Cao
AU  - Ziqing Zhu
AU  - Liang Shi
AU  - Bo Liu
AU  - Zhuo Ma
PY  - 2018
DA  - 2018/01/22
TI  - Multi-feature based event recommendation in Event-Based Social Network*
JO  - International Journal of Computational Intelligence Systems
SP  - 618
EP  - 633
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.48
DO  - 10.2991/ijcis.11.1.48
ID  - Cao2018
ER  -