Abstract: Services which are recommended to the mobile devices like PDAs, Cellular Phones, smart phones and Laptops while moving using ISAP (Information Service and Application Provider) are increased by accurately predicting user usage pattern. But these discovery may not be always be good enough since the differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work. Here CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern - Mine) algorithm is used to mine CTMSPs. Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) generates user clusters and similarities between mobile sequences are evaluated by Location-Based Service Alignment (LBS-Alignment).The specific time intervals to group the huge mobile logs are found using Genetic Algorithm based method called GetNTSP (Get Number of Time Segmenting Points).CTMSP-Mine technique, which creates CTMSPs utilizes Co-Smart-Cast and time intervals results. These patterns are used to predict the mobile user future behavior and service recommendations are given accordingly.