TY - GEN
T1 - Event participation recommendation in event-based social networks
AU - Ding, Hao
AU - Yu, Chenguang
AU - Li, Guangyu
AU - Liu, Yong
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Event-based Social Networks (EBSN) have experienced rapid growth in recent years. Event participation recommendation is to recommend a list of users who are most likely to participate in a new event. Due to the nature of new event and severe data sparsity in EBSN, the traditional recommender systems do not work well for event participation recommendation. In this paper, we first conduct a study of Meetup users to understand the major factors impacting their event participation decisions. We then develop a sliding-window based machine-learning model that effectively combines user features from multiple channels to recommend users to new events. Through evaluation using the Meetup dataset, we demonstrate that our model can capture the short-term consistency of user preferences and outperforms the traditional popularitybased and nearest-neighbor based recommendation models. Our model is suitable for real-time recommendation on practical EBSN platforms.
AB - Event-based Social Networks (EBSN) have experienced rapid growth in recent years. Event participation recommendation is to recommend a list of users who are most likely to participate in a new event. Due to the nature of new event and severe data sparsity in EBSN, the traditional recommender systems do not work well for event participation recommendation. In this paper, we first conduct a study of Meetup users to understand the major factors impacting their event participation decisions. We then develop a sliding-window based machine-learning model that effectively combines user features from multiple channels to recommend users to new events. Through evaluation using the Meetup dataset, we demonstrate that our model can capture the short-term consistency of user preferences and outperforms the traditional popularitybased and nearest-neighbor based recommendation models. Our model is suitable for real-time recommendation on practical EBSN platforms.
KW - Event participation recommendation
KW - Event-based social networks
KW - Social network analysis
KW - Temporal recommendation
UR - http://www.scopus.com/inward/record.url?scp=84995487647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995487647&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47880-7_22
DO - 10.1007/978-3-319-47880-7_22
M3 - Conference contribution
AN - SCOPUS:84995487647
SN - 9783319478791
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 361
EP - 375
BT - Social Informatics - 8th International Conference, SocInfo 2016, Proceedings
A2 - Spiro, Emma
A2 - Ahn, Yong-Yeol
PB - Springer Verlag
T2 - 8th International Conference on Social Informatics, SocInfo 2016
Y2 - 11 November 2016 through 14 November 2016
ER -