Type
Conference PaperKAUST Department
Computer ScienceComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-04Permanent link to this record
http://hdl.handle.net/10754/655954
Metadata
Show full item recordAbstract
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.2019.00141Conference/Event name
2019 IEEE 35th International Conference on Data Engineering (ICDE)Additional Links
https://ieeexplore.ieee.org/document/8731461/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8731461
ae974a485f413a2113503eed53cd6c53
10.1109/ICDE.2019.00141