Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 113-122.doi: 10.11896/jsjkx.230200105

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

Adaptive Location Recommendation Based on Time Slots Clustering and User Dynamic Similarity

ZHU Jun1,2, HAN Lixin2, ZONG Ping2, LIU Hongying3, XIE Ling3, LI Jingxian2   

  1. 1 School of Computer and Software,Nanjing Vocational University of Industry Technology,Nanjing 210023,China
    2 College of Computer and Information Engineering,Hohai University,Nanjing 211100,China
    3 College of Computer Science,Nanjing University of Science and Technology Zijin College,Nanjing 210023,China
  • Received:2023-02-16 Revised:2023-05-23 Online:2023-12-15 Published:2023-12-07
  • About author:ZHU Jun,born in 1987,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include machine learning and recommender systems.
  • Supported by:
    National Natural Science Foundation of China(41771251),Natural Science Foundation of the Higher Education Institutions of Jiangsu Province,China(21KJB520009) and Start-up Fund for New Talented Researchers of Nanjing Vocational University of Industry Technology(YK23-05-01).

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Abstract: Location recommendation is an important service for businesses and users in location-based social networks,and the recommended results are greatly influenced by user preferences and spatial-temporal contexts.Most existing researches ignore the variation in user similarity over time,lack adaptability when making recommendations,and suffer from serious data sparsity pro-blem.To address the issues above,this paper proposes an adaptive location recommendation algorithm(ALRTU) based on time slots clustering and user dynamic similarity.First,time slots clustering based on fuzzy c-means is devised according to the statistics of historical check-in data.Time similarity in each time cluster is calculated,and original ratings are updated further by app-lying smoothing technology to solve the problem of data sparsity.The dynamic similarities of users are calculated by hour.Diffe-rent rating subsets are selected adaptively according to time slots clustering and then realize user preferences and temporal influences mining.Second,users are classified on the basis of check-in frequency,and then kernel density estimation or power law distribution algorithm is selected adaptively to mine geographical features.Finally,user preferences and spatial-temporal contexts are effectively fused to produce location recommendations.Extensive offline experiments are conducted on two real-world datasets(Brightkite and Gowalla) to verify the accuracy.Experimental results show that the accuracy of ALRTU on Brightkite and Gowalla datasets is respectively improved by 3.74% and 1.42% on average,compared with the highest recommendation accuracy among the benchmark methods.

Key words: Location recommendation, Adaptive recommendation, Time slots clustering, Dynamic similarity, Spatial features

CLC Number: 

  • TP181
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