人群的呼唤：基于位置的社会服务中的事件参与
The Call of the Crowd: Event Participation in Location-based Social Services
摘要：
理解事件参与背后的社会和行为力量不仅从社会科学的角度来看是有趣的，而且在个性化事件推荐系统的设计中也有重要的应用。本文利用广泛使用的基于位置的社交网络Foursquare的数据，分析了三个大都市的事件模式。我们提出了几个假设，用户参与的激励因素，并确认社会方面发挥了重要作用，在确定用户参与活动的可能性。虽然一个明确的社会过滤信号占主导地位的因素是否朋友出席，一个事件的流行程度也被证明是一个强大的吸引力。此外，我们通过在高维图中执行随机游走来捕获隐含的社交信号，该高维图编码了朋友的地点类型偏好，并且证明特别适合于为用户识别相关的利基事件。我们的研究结果在何种程度上的各种时间，空间和社会方面的用户的事件偏好的基础，使我们进一步假设，因素的组合更好地模型用户的事件的兴趣。我们通过一个监督学习框架来验证这一点。我们发现，对于伦敦三分之一的用户和纽约和芝加哥五分之一的用户，它可以确定用户将参加的建议池中的确切事件。
英文摘要：
Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are attending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users' event preferences lead us to further hypothesize that a combination of factors better models users' event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
