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Using Mobile Services Based on SNS to Recommend Who, How, and When to Collaborate

机译:使用基于SNS的移动服务推荐谁,如何以及何时协作

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The rising popularity of smart phones and social networking services (SNS) is changing many aspects of people's collaboration. With the wide use of smart phones, collaborative work based on mobile web becomes loose and flexible. Collaborators have more chances to collaborate with other people anywhere at anytime. But most supporting tools for traditional computer supported cooperative work just support defined collaborative process for certain collaborators. They can't satisfy the new requirements for loose collaboration where collaborators, collaborative process and time are unknown. In this paper, we presentation approach to using three mobile services based on SNS and mobile sensor data to recommend who, how and when to collaborate. This collaborative approach based on mobile services solves three basic key problems of modern collaboration. Firstly, we collect abundant data from SNS, do the semantic analysis, and dig out the suitable collaborators. Secondly, by analyzing the data from calendars and smart phones, we figure out the situations which collaborators are in, then reason the suitable contacts by our novel rules and finally recommend whether we can call or not, as well as the ranked text contacts. Thirdly, we use the calendar information to recommend the common free time for collaborators to work together. To verify the effectiveness of the approach and accuracy of collaborative recommendations, we have implemented an app including the services on android platform and designed two independent experiments. The case studies show the approach provides an effective and accurate means for collaborative recommendations.
机译:智能手机和社交网络服务(SNS)的普及不断越来越多的人们合作的许多方面。随着智能手机的广泛使用,基于移动网的协作工作变得松动,灵活。合作者有机会随时随地与其他人合作。但传统计算机的最多支持工具支持的合作工作只是支持某些合作者的定义协作过程。他们无法满足宽松合作的新要求,其中合作者,协作过程和时间未知。在本文中,我们介绍了基于SNS和移动传感器数据的三个移动服务的方法,推荐谁,如何以及何时协作。基于移动服务的这种协作方法解决了现代协作的三个基本关键问题。首先,我们收集来自SNS的丰富数据,做出语义分析,并挖掘合适的合作者。其次,通过分析来自日历和智能手机的数据,我们找出了合作者所在的情况,然后推理我们的新规则的合适联系人,最后推荐我们是否可以调用或不调用,以及排名的文本联系人。第三,我们使用日历信息来推荐合作者共同努力的常见空闲时间。为了验证协作建议的方法和准确性的有效性,我们已经实施了一个应用程序,包括Android平台的服务,并设计了两个独立的实验。案例研究表明,该方法提供了合作建议的有效和准确的手段。

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