Personal APP behavior analysis based on mobile device networks

In the era of information network explosion, users can be connected to various forms of social networking sites anytime and anywhere through the mobile devices. Users interact between people with the way of social networks platforms. People interact with friends through the social networks platforms, and from the creation of the interests and preferences of people. Furthermore, for many brand enterprises, it is important to know how to understand personal preferences, because when you know the personal preference information, it can carry out personal preference for advertising, product recommendation, article recommended...and other diversified personal social service, which can increase the click rate and exposure of the products, better close to the needs of the user's life. Therefore, With the development trend of social, the research of the paper is mainly based on 〈〈the operation behavior of mobile devices 〉〉, such as: browse the page, click behavior, stay time...etc. And 〈〈Social interaction behavior〉〉, such as: user clicked fan page, user graffiti wall message, friend clicked fan page...etc' and from this huge amount of personal data to find out corresponding diverse group for personal preference category. We can by personal preference information for diversify personal advertising, product recommendation and other services. The paper at last through the actual business verification, on the APP, the total average of stay time increased by 34.32%, total average bounce rate of assignment reduced 30.79%, total average click through rate of assignment increased 47.18%, total average click through rate of commodity increased 47.75%, total average redemption rate of commodity increased 56.35%, total average purchase rate of the commodity increased 7.92 times. More fully represents the results of this research fit the use's preference.