SBIR Phase I: Real-Time Social Data Analytics to Resolve Lack of Trust in the Sharing Economy

这个小企业创新研究第一阶段项目将构建一个新的软件系统，提供实时社交数据分析，帮助人们评估在线分享经济中的信任水平。在共享经济中，两个不一定认识的人在网上交易商品或服务。参与者之间缺乏信任是共享经济需要解决的挑战之一。现有的方法过于昂贵(例如FICO评分)或过于僵化(例如Facebook API)，无法服务于各种类型的在线共享市场。这个项目将提供一个软件系统来处理/显示每个目标市场的实时社会分析，以便它可以显示两个参与者之间的共同背景(例如，朋友的朋友，或来自同一学校，或来自相同的工作场所等)。两个人的共同背景越多，他们在完成一笔交易时就越有可能感到舒服。主要的技术挑战是提供实时结果，同时保证将社交数据集添加到以前收集的数据集的灵活性。这个项目将通过使用图形数据库和相关的匹配算法来解决这些挑战。与SQL(结构化查询语言)相比，Graph数据库速度更快(无连接)，并允许更轻松地添加新数据集(无模式)。该项目更广泛的影响/商业潜力是通过提供一种解决信任缺失的机制来发展共享经济。共享经济目前是一个价值35亿美元的市场，每年增长25%，有可能增长到260亿美元。这个发展中的市场将创造新的就业机会，Airbnb(共享房间)和RelayRdes(共享汽车)等公司就证明了这一点。共享经济对社会是有益的，因为它鼓励重复使用原本未得到充分利用的资产，使资产所有者能够收回一些价值，并将环境浪费降至最低。正如许多文章中指出的那样，参与者之间缺乏信任是发展共享经济的最大挑战之一。该项目将通过实时揭示参与者现有的个人关系，帮助他们在参与者之间建立信任。此外，随着时间的推移积累的数据可能会被用来降低目前适用于共享经济中的商业的高额保险费。如果负结果的概率(即保险风险)可以与在线交易中两个参与者之间的共同背景联系在一起，那么本文提出的信任度量可以作为承保保险产品的新度量。

This Small Business Innovation Research Phase I project will build a novel software system to provide real-time social data analytics that will help people assess trust levels in the on-line sharing economy. In the sharing economy, two people who don't necessarily know each other trade goods or services on-line. Lack of trust among participants is one of the challenges that the sharing economy needs to resolve. Existing methods are too expensive (e.g. FICO score) or too inflexible (e.g. Facebook APIs) to serve the various types of on-line sharing markets. This project will provide a software system to process/display real time social analytics for each target market so that it can show the common background between two participants (e.g. a friend's friend, or from the same school, or from the same workplace, etc.) The more common background two people have, the more comfortable they are likely to feel in completing a transaction. The main technical challenge is to provide real time results while guaranteeing flexibility in adding social datasets to previously-collected datasets. This project will solve these challenges by using Graph Database and associated matching algorithms. Compared to SQL (Structured Query Language), Graph Database is faster (no JOINs) and allows easier addition of new datasets (no SCHEMAs). The broader impact/commercial potential of this project is to grow the sharing economy by providing a mechanism to resolve the lack of trust. The sharing economy is currently a $3.5 billion market, growing at 25% annually, with the potential to grow to $26 billion. This developing marketplace will create new jobs, as evidenced by companies such as AirBnB (sharing rooms) and RelayRides (sharing cars). The sharing economy is beneficial for society since it encourages the re-use of assets that would otherwise be underutilized, allowing asset owners to recover some value and minimizing environmental waste. As noted in many articles, lack of trust among participants is one of the biggest challenges to growing the sharing economy. This project will help build trust among participants by revealing their existing personal connections in real time. Furthermore, data accumulated over time can potentially be used to reduce the high insurance premiums that currently apply to commerce in the sharing economy. If the probability of a negative outcome (i.e. the insurance risk) can be tied to the common background between two participants in an on-line transaction, then the trust measure that is proposed here can be used as a novel metric to underwrite an insurance product.