In the last years, Learning to Rank (LtR) had a significant influence on several tasks in the Information Retrieval field, with large research efforts coming both from the academia and the industry. Indeed, efficiency requirements must be fulfilled in order to make an effective research product deployable within an industrial environment. The evaluation of a model can be too expensive due to its size, the features used and several other factors. This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time.
|Data di pubblicazione:||2017|
|Titolo:||Efficiency/Effectiveness Trade-offs in Learning to Rank|
|Titolo del libro:||ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1145/3121050.3121109|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|