Computer Science ›› 2019, Vol. 46 ›› Issue (10): 77-83.doi: 10.11896/jsjkx.180901757

• Big Data & Data Science • Previous Articles     Next Articles

Study on Point-of-interest Collaborative Recommendation Method Fusing Multi-factors

CHEN Jiong1, ZHANG Hu2,3, CAO Fu-yuan2,3   

  1. (Department of Computer Engineering,Shanxi Polytechnic College,Taiyuan 030006,China)1
    (School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)2
    (Key Laboratory of Ministry of Education for Computation Intelligence and Chinese InformationProcessing(Shanxi University),Taiyuan 030006,China)3
  • Received:2018-09-17 Revised:2018-12-23 Online:2019-10-15 Published:2019-10-21

Abstract: Point-of-interest (POI) recommendation is a task to recommend geographical locations that users may be interested in.It is an important researches in location-based social networks (LBSN) services.For the existing problems that POI recommendation currently has lower recommendation precision,lacks of personalization in recommendation results,and has poor integration of sentimental orientation factors,etc.,this paper proposed a POI collaborative recommendation model(GCSR) fusing multi-factors based on the comprehensive analysis of POI related influencing factors,such as geographical location,category preference,popularity,social and sentimental orientation and so on.Firstly,the geographical relevance score is calculated based on POI geographical location data.Secondly,category preference score is defined according to users’ category preference and POI popularity.Then,the strength of the social relationships between users is calculated based on the social relationships,the sentimental orientation score of users is calculated by mining the comment text,and the two are effectively combined with the collaborative filtering recommendation technology to obtain the social sentiment score.Finally,geographical relevance score,category preference score and social sentiment score are effectively integrated to recommend Top-N POI.Multiple comparative experiments conducted on Foursquare’s real check-in datasets demonstrate that the GCSR model achieves better recommendation effect,with an ave-rage improvement of 1.7% and 0.6% in precision and recall,compared with the best effective JRA in the baseline models.

Key words: Geographical location, Location-based social networks, Point-of-interest recommendation, Sentimental orientation, Social relationships

CLC Number: 

  • TP391
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