Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDE.2019.00141 | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Keywords | Field | DocType |
Cost function,Matrix decomposition,Task analysis,Measurement,Jacobian matrices,Linear programming | Small number,Data mining,Collaborative filtering,Task analysis,Computer science,Matrix decomposition,Linear programming,Point of interest,Area under the roc curve,Maximization | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-5386-7474-1 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Han | 1 | 23 | 6.11 |
| Shang Shuo | 2 | 384 | 25.35 |
| Aixin Sun | 3 | 3071 | 156.89 |
| Peilin Zhao | 4 | 1365 | 80.09 |
| Kai Zheng | 5 | 936 | 69.43 |
| Panos Kalnis | 6 | 3297 | 141.30 |