Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11): 132-142.doi: 10.11896/jsjkx.230400045

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

Natural Noise Filtering Algorithm for Point-of-Interest Recommender Systems

ZHU Jun1,2, HAN Lixin2, ZONG Ping2, XU Yiqing1, XIA Ji’an1, TANG Ming1   

  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
  • Received:2023-04-07 Revised:2023-07-04 Online:2023-11-15 Published:2023-11-06
  • 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: The inherent natural noise in the original dataset of recommender systems(RSs) causes error and interference to re-commendation algorithms.Existing studies pay more attention to the malicious noise represented by various security attacks.The natural noise which is more subtle and difficult to deal with has rarely been documented.Most researches about natural noise are conducted for conventional RSs.However,the data feature and the causes and forms of natural noise in point-of-interest(POI) RSs are all different from those in conventional RSs.To filter the natural noise for POI RSs,a novel natural noise filtering method(NFDC) based on dispersion quantification and clustering distance analysis is proposed.The dispersion of a subset of the original check-in dataset is defined and calculated to indicate the data-driven uncertainty,and the accuracy metric F1 is adopted to represent the prediction-driven uncertainty.The measures of dispersion and accuracy metric vectors are empirically categorized to identify the proportion of the potential noise.The fuzzy C-means-based denoi-sing algorithm is performed to analyze the similarity of user behavior patterns and then screen the potentially noisy points based on clustering distance analysis.A customized rule is designed to further verify and delete the natural noise.Extensive experiments are conducted on two real-world location-based social network datasets,Brightkite and Gowalla.The datasets processed by NFDC and the other four benchmark algorithms are respectively input into five representative POI recommendation algorithms for comparison.Experimental results show that NFDC effectively filters the natural noise and provides reliable input for RSs.Compared with the highest accuracy supported by other denoi-sing methods,the accuracy in NFDC-processed Brightkite and Gowalla datasets is respectively improved by 15.95% and 5.00% on average.

Key words: Recommender system, Point-of-Interest recommendation, Natural noise, Uncertainty, Dispersion, Clustering

CLC Number: 

  • TP181
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