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    AUC-MF: Point of Interest Recommendation with AUC Maximization

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    Type
    Conference Paper
    Authors
    Han, Peng
    Shang, Shuo
    Sun, Aixin
    Zhao, Peilin
    Zheng, Kai
    Kalnis, Panos cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-04
    Permanent link to this record
    http://hdl.handle.net/10754/655954
    
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    Abstract
    The task of point of interest (POI) recommendation aims to recommend unvisited places to users based on their check-in history. A major challenge in POI recommendation is data sparsity, because a user typically visits only a very small number of POIs among all available POIs. In this paper, we propose AUC-MF to address the POI recommendation problem by maximizing Area Under the ROC curve (AUC). AUC has been widely used for measuring classification performance with imbalanced data distributions. To optimize AUC, we transform the recommendation task to a classification problem, where the visited locations are positive examples and the unvisited are negative ones. We define a new lambda for AUC to utilize the LambdaMF model, which combines the lambda-based method and matrix factorization model in collaborative filtering. Experiments on two datasets show that the proposed AUC-MF outperforms state-of-the-art methods significantly in terms of recommendation accuracy.
    Citation
    Han, P., Shang, S., Sun, A., Zhao, P., Zheng, K., & Kalnis, P. (2019). AUC-MF: Point of Interest Recommendation with AUC Maximization. 2019 IEEE 35th International Conference on Data Engineering (ICDE). doi:10.1109/icde.2019.00141
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2019 IEEE 35th International Conference on Data Engineering (ICDE)
    DOI
    10.1109/ICDE.2019.00141
    Additional Links
    https://ieeexplore.ieee.org/document/8731461/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8731461
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDE.2019.00141
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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