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Contextualized mobile recommendation service based on interactive social network discovered from mobile users

机译：基于从移动用户发现的交互式社交网络的上下文化移动推荐服务

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摘要

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person's activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor's contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as prepositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.
机译：个人上下文是在特定时间和地点提供上下文化移动推荐服务的最重要信息。但是，服务提供商很难了解个人情况，因为每个人的活动和偏好都非常模棱两可，并取决于众多未知因素。为了解决这个问题，我们专注于发现人与人之间的社会关系（例如，家人，朋友，同事等）。我们假设某个人的个人背景与其他人的背景相互关联，并研究了如何利用邻居的背景，这可能会对他的个人背景产生有意义的影响。它表明我们必须发现隐式的社交网络来表达人与人之间的上下文依赖性。因此，在本文中，我们提出了一种通过与人类专家互动来构建有意义的社交网络的互动方法。给定某种社会关系（例如isFatherOf），此提议的系统可以评估人类专家断言的一组条件（表示为介词公理），并向他们展示由数据挖掘工具产生的社交网络。更重要的是，已经利用社交网络本体论来通过证明条件是否经过逻辑验证来一致地指导它们，并完善发现的社交网络。我们希望这些社交网络适用于生成基于上下文的推荐服务。在此研究项目中，我们已通过从大约200万用户中收集数据集，将提出的系统应用于发现移动用户之间的社交网络。

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