Tutorial: Efficiency/Effectiveness Trade-offs in Learning to Rank

May 03 2018

Accepted at ECML-PKDD ’18: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases [1].

Abstract. In the last years, learning to rank (LtR) had a significant influence on several data mining tasks and in particular 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.

Tutorial material available at: http://learningtorank.isti.cnr.it/.


[1]   Claudio Lucchese and Franco Maria Nardini. Efficiency/effectiveness trade-offs in learning to rank. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, 2018.

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