Event Participation Recommendation in Event-Based Social Networks

2016 ◽  
pp. 361-375 ◽  
Author(s):  
Hao Ding ◽  
Chenguang Yu ◽  
Guangyu Li ◽  
Yong Liu
Keyword(s):  
Social Networks ◽  
Event Based

Joint Event-Partner Recommendation in Event-Based Social Networks

2018 ◽  
Author(s):  
Hongzhi Yin ◽  
Lei Zou ◽  
Quoc Viet Hung Nguyen ◽  
Zi Huang ◽  
Xiaofang Zhou
Keyword(s):  
Social Networks ◽  
Joint Event ◽  
Event Based

Deep Learning Driven Venue Recommender for Event-Based Social Networks

2020 ◽  
Vol 32 (11) ◽  
pp. 2129-2143 ◽  
Author(s):  
Soumajit Pramanik ◽  
Rajarshi Haldar ◽  
Anand Kumar ◽  
Sayan Pathak ◽  
Bivas Mitra
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Event Based

scholarly journals Activeness and Loyalty Analysis in Event-Based Social Networks

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 119
Author(s):  
Thanh Trinh ◽  
Dingming Wu ◽  
Joshua Zhexue Huang ◽  
Muhammad Azhar
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Windows ◽  
Social Groups ◽  
Time Frame ◽  
Machine Learning Techniques ◽  
Consecutive Time ◽  
Highly Correlated ◽  
Event Based ◽  
Association Matrix

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.

scholarly journals Interaction-Aware Arrangement for Event-Based Social Networks

2019 ◽  
Author(s):  
Feifei Kou ◽  
Zimu Zhou ◽  
Hao Cheng ◽  
Junping Du ◽  
Yexuan Shi ◽  
...  
Keyword(s):  
Social Networks ◽  
Event Based
Sign in / Sign up

Export Citation Format

Share Document