Acta Geodaetica et Cartographica Sinica ›› 2018, Vol. 47 ›› Issue (9): 1261-1269.doi: 10.11947/j.AGCS.2018.20170552

Previous Articles     Next Articles

A Point-of-interest Recommendation Method Based on Hawkes Process

ZHANG Guoming1,2, WANG Junshu3,4, JIANG Nan3,4, SHENG Yehua3,4   

  1. 1. Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
    2. Health Statistics and Information Center of Jiangsu Province, Nanjing 210008, China;
    3. Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
    4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2017-09-25 Revised:2018-03-23 Online:2018-09-20 Published:2018-09-26
  • Supported by:
    The National Natural Science Foundation of China (No. 41631175);The National Natural Science Foundation of Jiangsu Province (No. BK20171037);The Program of Natural Science Research of Jiangsu colleges and Universities (No. 17KJB170010)

Abstract: Point-of-interest (POI) recommendation is a crucial personalized location service in LBSNs.To cope with the complexity and extreme sparsity of users check-in data,we proposed a context-aware collaborative filtering POI recommendation algorithm based on Hawkes process (HWCF).First,we analyzed users' behavior characteristics according to the geographic spatial clustering phenomenon of users' check-in POI,and filtered users' candidate POI.Then,we utilized Hawkes process to model candidate POI.Integrated different context information,such as spatial distance,spatial sequence transformation,temporal,users' preferences,POI popularity,etc.to compute the visiting probability of candidate POI for every user,and then obtained the top-k recommendation list by sorting the visiting probability.Finally,we discussed the range and adjustment of parameters in HWCF algorithm.Experimental results show that HWCF achieves better performance compared to other advanced POI recommendation algorithms.

Key words: point-of-interest recommendation, location based social network, Hawkes process

CLC Number: