CONTEXTUALIZED RECOMMENDATION BASED ON REALITY MINING FROM MOBILE SUBSCRIBERS

It is difficult to be aware of the personal context for providing a mobile recommendation, because each person's activities and preferences are ambiguous and depend upon numerous unknown factors. In order to solve this problem, we have focused on a reality mining to discover social relationships (e.g., family, friends, etc.) between people in the real world. We have assumed that the personal context for any given person is interrelated with those of other people, and we have investigated how to take into account a person's neighbor's contexts, which possibly have an important influence on his or her personal context. This requires that given a dataset, we have to discover the hidden social networks which express the contextual dependencies between people. In this paper, we propose a semiautomatic approach to build meaningful social networks by repeating interactions with human experts. In this research project, we have applied the proposed system to discover the social networks among mobile subscribers. We have collected and analyzed a dataset of approximately two million people.

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