A new item recommend algorithm of sparse data set based on user behavior analyzing

The e-commercial systems have experienced a rapid development and have brought great benefit to people´s daily life. Many e-commercial systems use recommend algorithm to filter irrelative information and recommend items to users. One of the most popular recommend algorithms is Collaborative Filtering Algorithm. However, there are some shortcomings in Collaborative Filtering Algorithm, causing that the algorithm cannot be well applied when the data set is sparse. This paper proposes a new item recommend algorithm which based on the analysis and prediction of user behavior by pattern recognition and statistic model that can be applied on sparse user behavior data set, avoiding the problems Collaborative Filtering Algorithm faced when the data set is sparse.