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Exploring the Choice Under Conflict for Social Event Participation

机译:探索社会事件参与冲突下的选择

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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.
机译:近年来已经见证了活动驱动的SNS的蓬勃发展,允许网络陌生人在物理世界中连接。这一新的商业模式对活动组织者施加挑战,绘制事件计划并预测出勤率。直观地,这些服务依赖于用户偏好的准确估计。但是,由于历史参与的各种动机(即出席者可能绝对不明确表示利益),传统的推荐技术可能无法揭示可靠的用户配置文件。与此同时,通过用户可能面对邀请冲突的现象(即同时收到的多种邀请,其中只能接受少数人),我们意识到这些选择可能反映真实的偏好。在这篇文章中,在本文中,我们开发了一种基于冲突选择的模型,以在面对冲突时重建用户的决策过程。具体而言,在选择模型的效用的角度下,我们制定用户与整合内容,社会和成本的因素的倾向,因此旨在捕获局部兴趣以及潜在的社交互动。此外,我们将冲突选择三元组的选择转移到成对排名任务中,并引入了基于学习的基于优化方案来解决问题。关于现实数据集的综合实验表明,我们的框架可能优于最先进的基本线具有重要的基础,这验证了冲突和选择可以更好地解释用户的真实偏好的假设。

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