Exploring the Choice Under Conflict for Social Event Participation

Recent years have witnessed the booming of event driven SNS, which allow cyber strangers to get connected in physical world. This new business model imposes challenges for event organizers to draw event plan and predict attendance. Intuitively, these services rely on the accurate estimation of users...

Full description

Saved in:
Bibliographic Details
Published inDatabase Systems for Advanced Applications pp. 396 - 411
Main Authors Zhao, Xiangyu, Xu, Tong, Liu, Qi, Guo, Hao
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2016
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN3319320246
9783319320243
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-32025-0_25

Cover

Loading…
More Information
Summary:Recent years have witnessed the booming of event driven SNS, which allow cyber strangers to get connected in physical world. This new business model imposes challenges for event organizers to draw event plan and predict attendance. Intuitively, these services rely on the accurate estimation of users’ preferences. However, due to various motivation of historical participation(i.e. attendance may not definitely indicate interests), traditional recommender techniques may fail to reveal the reliable user profiles. At the same time, motivated by the phenomenon that user may face to conflict of invitation (i.e. multiple invitations received simultaneously, in which only a few could be accepted), we realize that these choices may reflect real preference. Along this line, in this paper, we develop a novel conflict-choice-based model to reconstruct the decision-making process of users when facing to conflict. To be specific, in the perspective of utility in choice model, we formulate users’ tendency with integrating content, social and cost-based factors, thus topical interests as well as latent social interactions could be both captured. Furthermore, we transfer the choice of conflict-choice triples into the pairwise ranking task, and a learning-to-rank based optimization scheme is introduced to solve the problem. Comprehensive experiments on real-world data set show that our framework could outperform the state-of-the-art baselines with significant margin, which validates the hypothesis that conflict and choice could better explain user’s real preference.
ISBN:3319320246
9783319320243
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-32025-0_25