AUC-MF: Point of Interest Recommendation with AUC Maximization

Peng Han, Shuo Shang, Aixin Sun, Peilin Zhao, Kai Zheng, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2019 IEEE 35th International Conference on Data Engineering (ICDE)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781538674741
StatePublished - Apr 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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