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UvA-DARE ( Digital Academic Repository ) Query-level Ranker Specialization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Jagerman Rijke, De |
| Copyright Year | 2017 |
| Abstract | Traditional Learning to Rank models optimize a single ranking function for all available queries. is assumes that all queries come from a homogenous source. Instead, it seems reasonable to assume that queries originate from heterogenous sources, where certain queries may require documents to be ranked dierently. We introduce the Specialized Ranker Model which assigns queries to dierent rankers that become specialized on a subset of the available queries. We provide a theoretical foundation for this model starting from the listwise Placke-Luce ranking model and derive a computationally feasible expectation-maximization procedure to infer the model’s parameters. Furthermore we experiment using a noisy oracle to model the risk/reward tradeo that exists when deciding which specialized ranker to use for unseen queries. ACM Reference format: Rolf Jagerman, Harrie Oosterhuis, and Maarten de Rijke. 2017. erylevel Ranker Specialization. In Proceedings of the first International Workshop on LEARning Next gEneration Rankers, Amsterdam, October 1, 2017 (LEARNER 2017), 5 pages. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://pure.uva.nl/ws/files/23588425/LEARNER2017_full_2.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
