Abstract: Since the consideration of location recommendation algorithms based on LBSNs (Location-Based Social Networks) is too single,and it couldn’t effectively solve the problem of location recommendation for user in different ci-ties,synthesizing the factors of potential social influence,content match influence and geographical property influence,the personalized location recommendation algorithm SCL (Social-Content-Location) based on user check-ins and geographical properties was proposed. SCL algorithm introduces the comparison of users’ interest features based on the collaborative filtering,and it improves the similarity of users.At the same time,when the content information of location is analyzed, user’s comments on location is integrated,and it alleviates the influence of the short text feature of location labels to LDA (Latent Dirichlet Allocation) topic extraction and improves the accuracy of user’s interest and city pre-ference topic in extraction.The experimental results show that,for the recall rate of residence city,algorithm SCL outperforms collaborative filtering algorithm U near 65%,and outperforms algorithm LCA-LDA near 30%.For the recall rate of new city,algorithm SCL outperforms algorithm LCA-LDA near 26%,which shows that algorithm SCL has certain feasibility for location recommendation under different cities.

Key words: Potential social influence,Content match influence,Geographical property influence,Collaborative filtering,LDA topic extraction