Learning Rankings for Information Retrieval
author:
Thorsten Joachims,
Cornell University
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| Slides | |
| 0:00 | Learning Rankings for Information Retrieval |
| 0:35 | Ranked Information Retrieval |
| 1:49 | Learning Ranking Functions in IR |
| 3:45 | Rankings as Structured Outputs |
| 4:58 | Why Predict a Ranking? |
| 6:01 | Why Predict a Ranking?1 |
| 7:49 | Why Predict a Ranking?2 |
| 9:23 | Why Predict a Ranking?3 |
| 10:44 | Training to Optimize Ranking Performance |
| 11:11 | Overview of Argument |
| 12:26 | Related Work |
| 15:03 | Non-Linear Classification Loss |
| 17:33 | Multivariate Classification |
| 18:24 | Support Vector Machine |
| 18:53 | Multivariate Classification SVM |
| 21:25 | Multivariate SVM Optimization Problem |
| 24:52 | Multivariate SVM Generalizes Classification SVM |
| 25:28 | Sparse Approximation Algorithm |
| 27:34 | Polynomial Convergence Bound |
| 28:07 | ARGMAX |
| 29:16 | Multivariate Ranking SVM |
| 31:10 | Multivariate Ranking SVM for ROC Area |
| 32:58 | ARGMAX for ROC-Area |
| 34:09 | Multivariate ROC SVM |
| 35:05 | Multivariate Ranking SVM for AvgPrec |
| 35:47 | ARGMAX for Average Precision |
| 37:04 | Experiment: Generalization Performance |
| 38:52 | Experiment: Number of Iterations |
| 39:17 | Implementation in SVMstruct |
| 39:49 | Conclusions |
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