A New Mobile Recommendation Algorithm Based on Statistical Theory

With Recommendation technology has been widely used in advertising push, e-commerce and other fields and it has shown its powerful application prospect. But with the index increasing of mobile commerce data size, the size of the recommendation system is also increased and this leads to that the traditional collaborative filtering recommendation algorithm cannot adapt to such a big data processing. To solve the problem, we proposed an algorithm based on the statistical analysis of user data. First, this algorithm classified the data simply, and then we could gain the relatively accurate personalized recommendation results by the statistical analysis of different attributes on the data sets.