AUC-MF: Point of Interest Recommendation with AUC Maximization

Engineering

• Tasks 100%
• Maximization 100%
• Classification Performance 50%
• State-of-the-Art Method 50%
• Matrix Factorization 50%
• Classification Problem 50%
• Filtration 50%
• Sparsity 50%

Computer Science

• Points of Interest 100%
• Sparsity 25%
• Collaborative Filtering 25%
• Matrix Factorization Model 25%
• Imbalanced Data 25%
• Recommendation Accuracy 25%
• Positive Example 25%
• Classification Performance 25%
• Data Distribution 25%
• Classification Problem 25%

Keyphrases

• Recommendation Tasks 12%
• Imbalanced Distribution 12%