Analysis of Adaptive Training for Learning to Rank in Information Retrieval
| Autor: | Prasad Pradip Joshi, Saar Kuzi, Shubhra Kanti Karmaker Santu, Sahiti Labhishetty, ChengXiang Zhai |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Calibration (statistics) business.industry media_common.quotation_subject 05 social sciences Machine learning computer.software_genre Training (civil) Ranking (information retrieval) Search engine Ranking Robustness (computer science) Feature (machine learning) Learning to rank Artificial intelligence 0509 other social sciences 050904 information & library sciences business Function (engineering) computer media_common |
| Zdroj: | CIKM |
| Popis: | Learning to Rank is an important framework used in search engines to optimize the combination of multiple features in a single ranking function. In the existing work on learning to rank, such a ranking function is often trained on a large set of different queries to optimize the overall performance on all of them. However, the optimal parameters to combine those features are generally query-dependent, making such a strategy of "one size fits all" non-optimal. Some previous works have addressed this problem by suggesting a query-level adaptive training for learning to rank with promising results. However, previous work has not analyzed the reasons for the improvement. In this paper, we present a Best-Feature Calibration (BFC) strategy for analyzing learning to rank models and use this strategy to examine the benefit of query-level adaptive training. Our results show that the benefit of adaptive training mainly lies in the improvement of the robustness of learning to rank in cases where it does not perform as well as the best single feature. |
| Databáze: | OpenAIRE |
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