Abstract: With the development and popularization of smart mobile devices,spatial positioning technology continues to develop,and based on this,location-based social network is widely used.The majority of users check in LBSN,and comment on check-in activity,which not only record the spatial-temporal behavior track,but also provide great opportunities for us to study user behavior patterns and characteristics of preference.This paper proposed a commercial site re-commendation system based on LBSN.Firstly,it analysed the characteristics about the check-in time,the check-in location and the category of check-in retail in LBSN.Then it proposed four kinds of factors that affect retail location:diversity,competitive,relevance,passenger flow.Finally the system was implemented that can provide the best candidate based on various factors.The paper used those as the basis for experiments to verify the recommendation result.The results comply with the relevant expectations.

Key words: LBSN,Behavior track data,Commercial site