Computer Science ›› 2019, Vol. 46 ›› Issue (12): 63-68.doi: 10.11896/jsjkx.190400440

• Big Data & Data Science • Previous Articles     Next Articles

Community Detection Based Point-of-interest Recommendation

GONG Wei-hua, SHEN Song   

  1. (School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2019-04-17 Online:2019-12-15 Published:2019-12-17

Abstract: In recent years,LBSN (Location-based Social Networks) has attracted more and more attention as a typical heterogeneous information network.In view of the sparse check-in information of users in LBSN,this paper proposed a recommendation algorithm CBR (Community-Based Recommendation) based on community detection.It first calculates the similarity between the target user and the clustered interest topic cluster on the social media layer,and then calculates the user’s membership degree on the geographic cluster through the association matrix R between the interest topic cluster and the geographic cluster.Then it further integrates user’s social relationship to get user’s preference scores for each point of interest,and finally sorts according to the interest scores to achieve the Top-k recommendation.The experimental results show that the proposed algorithm can significantly improve the recommendation quality of the points of interest.

Key words: Multidimensional relationship, Community detection, Point-of-interest recommendation, Geographic cluster

CLC Number: 

  • TP391
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