The people interest will often be changed by the dynamic of environment. We proposed the Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network (DTPCS), combining user dynamic similarity and contextual information. Through the Community Network, the friends of similar interests could be found by the check-in information. And give different weight value as the intimacy. We considered the changes of user requirements in different contexts, and explore the impact of contextual factors. The different contextual information will give different weight value to be as a basis for recommendation. Finally, the system sorts the recommend sequence to be a Top-N recommendation list. According to the simulation results, in recommend error, DTPCS is less than the others about 35 %. In recommend calculation time, DTPCS is less than the others about 32 %. In recommend coverage, DTPCS is more than the others about 27 %.
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