Abstract
Analyzing users’ behavior is crucial to design recommendation systems on online service in IoT environment. It was difficult, if not impossible, to trace individual user’s specific behavior in a large scale. There are so much mobile online applications to produce some services, which are also becoming more and more popular and common methods with the advent of smart phones in IoT environment. Therefore, it is possible to collect large-scale dataset on mobile service markets. In this paper, we discuss cross-recommendation depending on the data analysis with large scale data collected from online services. We classified service popularity and users into two groups: mainstream and long tail. The described cross-recommendation between the two groups depends on commonly shared users’ attributes. The findings can use infer personalized recommendation to find niche market.
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