Mobile Advertising Predicted Conversion Rate Model a Recommendation System with Machine Learing Approach

With the development of mobile internet technology, there have a enormous pontential in mobile advertisement. But how we use this recources becomes a big problem. Fortinately, we can use a recommendation to recommend the advertise for the peolpe the may like it. This is called a excatly advertising putting. It can help the people to get the really information they want, also it can cut down the cost of the company, the can get the consumer approach from which platform, and pay for the cost in datadflow. And the collected data can help the company analysis the user's distribution so that the can improve the production and advertising. So Exactly advertising is one of the most important thing, the effect of advertising, usually measure by clicking and conversion rate in each link, most advertising system by advertising effect data return as the delivery efficiency measure standard to carry out optimization through exposure or click. But how we can trace the user behavior and predicted the advertisement conversion rate. Tecent use the pCVR(Predicted Conversion Rate), to help advertisers tracking advertising.This topic based on the mobile App advertising as the research object, to predict the probability of App ad Click after the activated which is a given advertising, the user and the context condition of advertising is the probability of click after activation. We will try to use KNN, random forest, User-Based top-N recommendation, Time Series model to set up a predict model and verification it in the last for this problem.