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Combined Regression and Ranking by Mind Map: Combined Regression and 
Ranking
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Combined Regression and Ranking

1. INTRODUCTION

2. BACKGROUND

2.3 Supervised Ranking Methods

2.4 Loss Functions

2.4.1 Squared Loss

2.4.2 Logistic Loss

2.4.3 Other Loss Functions

3. ALGORITHM: CRR

3.1 General Framework

3.2 Efficient Computation

3.2.1 Convergence

3.2.2 Efficient Sampling from P

3.2.3 Scalability

3.3 Non-Linear Models

4. EXPERIMENTS

4.1 Performance Metrics

4.1.1 Mean Squared Error (MSE)

4.1.2 AUC Loss

4.1.3 Mean Average Precision (MAP)

4.1.4 NDCG: Normalized Discounted Cumulative Gain

4.2 RCV1 Experiments

4.2.1 RCV1 Experimental Setup

4.2.2 RCV1 Results, Table 1, Table 2

4.3 LETOR Experiments

4.3.1 LETOR Experimental Setup

4.3.2 LETOR Results

4.4 Predicting Clicks in Sponsored Search

4.4.1 Regression and Ranking for Sponsored Search, Table 3

4.4.2 Click-Prediction Experimental Setup

4.4.3 Click-Prediction Results

6. CONCLUSIONS

5. RELATED WORK

5.1 Regression

5.2 Books

Information Retrieval, Research and Information Management, Information Storage and Retrieval