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首页> 外文期刊>User modeling and user-adapted interaction >Modeling real-time data and contextual information from workouts in eCoaching platforms to predict users' sharing behavior on Facebook
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Modeling real-time data and contextual information from workouts in eCoaching platforms to predict users' sharing behavior on Facebook

机译：从ecroacing平台中的锻炼中建模实时数据和上下文信息，以预测Facebook上的用户共享行为

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摘要

eCoaching platforms have become powerful tools to support users in their day-to-day physical routines. More and more research works show that motivational factors are strictly linked with the user inclination to share her fitness achievements on social media platforms. In this paper, we tackle the problem of analyzing and modeling users' contextual information and real-time training data by exploiting state-of-the-art classification algorithms, to predict if a user will share her current running workout on Facebook. By analyzing user's performance, collected by means of an eCoaching platform for runners, and crossing them with contextual information such as the weather, we are able to predict with a high accuracy if the user will post or not on Facebook. Given the positive impact that social media posts have in these scenarios, understanding what are the conditions that lead a user to post or not, can turn the output of the classification process into actionable knowledge. This knowledge can be exploited inside eCoaching platforms to model user behavior in broader and deeper ways, to develop novel forms of intervention and favor users' motivation on the long term.
机译：ecoaching平台已成为支持用户在日常物理例程中的强大工具。越来越多的研究工作表明，激励因素与用户倾向严格相关，以在社交媒体平台上分享她的健身成就。在本文中，我们通过利用最先进的分类算法来解决分析和建模用户的上下文信息和实时培训数据的问题，以预测用户将在Facebook上共享她当前的运行锻炼。通过分析用户的性能，通过针对跑步者的ecoach平台收集，并通过诸如天气之类的上下文信息交叉，如果用户将在Facebook上邮寄，则能够以高精度预测。鉴于社交媒体帖子在这些场景中的积极影响，了解导致用户发布的条件是什么，可以将分类过程的输出转换为可操作的知识。这种知识可以利用在地块平台内，以更广泛更深的方式模拟用户行为，培养新颖的干预形式，并赞有长期用户的动机。

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