In this paper, we present the architectural details of Odin Help Engine, a novel social search engine that leverages online social networks and sensing data from mobile devices to find targeted answers for subjective queries and recommendation requests. In Odin, users' queries are routed to those connections in their social network who (i) are most likely to help answer the question and (ii) can do so with a high level of confidence. Specifically, we first apply a link-based latent variable model to infer social relationships between users from their social network data to form a strength-weighted relationship graph. We then infer users' expertise by context mining from social network data as well as from their mobile device sensor data. Lastly we apply pagerank-like algorithm that takes both relationship strength and user expertise into account to find a person that is most likely willing to answer the question posted by the user. We present the general design of the architecture and outline a location-related query example for detailed illustration.
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机译：在本文中，我们介绍了Odin Help Engine的体系结构细节，Odin Help Engine是一种新颖的社交搜索引擎，该引擎利用在线社交网络和感测来自移动设备的数据来找到针对主观查询和推荐请求的目标答案。在Odin中，用户的查询被路由到他们的社交网络中的那些连接，这些连接（i）最有可能帮助回答问题，并且（ii）可以高度自信地这样做。具体而言，我们首先应用基于链接的潜在变量模型，以根据用户的社交网络数据推断用户之间的社交关系，以形成强度加权关系图。然后，我们通过从社交网络数据以及他们的移动设备传感器数据进行上下文挖掘来推断用户的专业知识。最后，我们应用类似于页面等级的算法，该算法将关系强度和用户专业知识都考虑在内，以找到最有可能愿意回答用户发布的问题的人。我们介绍了体系结构的总体设计，并概述了与位置有关的查询示例以进行详细说明。
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