A Mobile Recommendation Algorithm Based on Statistical Analysis of User Data

Recommendation technology is used to help people solve the problem of information overload. Recent years, it has been widely applied to the movie ratings, e-commerce and many other fields. Researchers have noticed its powerful application prospect. But with the exponential growth of information data, the recommendation systems also have to improve the ability of data processing and this leads to that the traditional collaborative filtering recommendation algorithms cannot meet the needs of the users. To solve the problem, we designed an algorithm based on the theory of statistical analysis. This algorithm classified the data simply firstly, and then system could give users the relatively satisfactory personalized recommendations by the statistical analysis of different attributes on the data sets.

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