To extract features from Deep Packet Inspection (DPI) data and perform mobile application recommendation, using the DPI data collected from Internet Service Provider (ISP) in Jiangsu Telecom, the access history data of active users defined by the communications operator was processed by matrix factorization recommendation (including Singular Value Decomposition (SVD) and Non-negtive Matrix Factorization (NMF)), SVD recommendation and factorization machine recommendation algorithms for mobile phone application recommendation. The results show that factorization machine algorithm achieves better performance, it means that factorization machine algorithm can better describe the latent connection in the user-item relationship.