Abstract
In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what rankers have performed well in the past. Multileaving methods offer an attractive approach to this problem since they can efficiently use online feedback to simultaneously evaluate a potentially arbitrary number of rankers. In this talk we discuss some of the main challenges in multileaving, and discuss promising areas for future research.
| Original language | English |
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| Title of host publication | Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017) |
| Editors | Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego |
| Number of pages | 2 |
| Publisher | CEUR-WS.org |
| Publication date | 2017 |
| Publication status | Published - 2017 |
| Event | 1st International Workshop on LEARning Next gEneration Rankers - Amsterdam, Netherlands Duration: 1 Oct 2017 → 1 Oct 2017 Conference number: 1 |
Workshop
| Workshop | 1st International Workshop on LEARning Next gEneration Rankers |
|---|---|
| Number | 1 |
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 01/10/2017 → 01/10/2017 |
| Series | CEUR Workshop Proceedings |
|---|---|
| Volume | 2007 |
| ISSN | 1613-0073 |