Computer Science ›› 2020, Vol. 47 ›› Issue (9): 81-87.doi: 10.11896/jsjkx.191100120

• Database & Big Data & Data Science • Previous Articles     Next Articles

Topic-Location-Category Aware Point-of-interest Recommendation

MA Li-bo, QIN Xiao-lin   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2019-11-15 Published:2020-09-10
  • About author:MA Li-bo,born in 1997,postgraduate.His main research interests include data management and recommendation system.
    QIN Xiao-lin,born in 1953,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include spatio-temporal database,distributed data management and security,etc.
  • Supported by:
    National Natural Science Foundation of China (61373015,61728204).

Abstract: With the continuous development of Location-Based Social Networks(LBSN),Point-of-Interest(POI) recommendations that help users explore new locations and merchants discover potential customers has received widespread attention.How-ever,due to the high sparsity of the users’ check-in data,POI recommendation faces serious challenges.To cope with this challenge,this paper explores the textual information,geographic information,and category information,incorporating interest topics,geographical influence,and category preference factors effectively,and proposes a topic-location-category aware collaborative filtering algorithm called TGC-CF for POI recommendation.The proposed algorithm uses the Latent Dirichlet Allocation(LDA) model to learn the interest topics distribution of users and calculate the similarity of interest topics distribution among users by mining textual information associated with POIs,models geographical influence by combining geographic distance and user’s regionalpre-ference,uses the TF-IDF statistical method to assess the target user’s preference for the category and consider the impact of other users’ category preference in the recommendation process,and finally integrate these influencing factors into a collaborative filtering recommendation model to generate a list of recommendations containing POIs that users are interested in.Experimental results on two real data sets show that TGC-CF algorithm performs better than other recommendation algorithms.

Key words: Location-based social networks, POI recommendation, Topic model, Geographical influence, Collaborative filtering

CLC Number: 

  • TP311
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