Tutorial on Learning To Rank

Efficiency/Effectiveness Trade-offs in Learning to Rank” tutorial by Claudio Lucchese and Franco Maria Nardini at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2018. 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.

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  • InstructionsDOCX

    Instruction file for the Tutorial

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  • Slides-Part1PDF

    First deck of slides for the tutorial

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  • Slides-Part2PDF

    Second deck of slides for the tutorial

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  • Slides-Part3PDF

    Third deck of slides for the tutorial

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  • HandsOn-1ipynb

    First Jupyter Notebook

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    Jupyter Notebook 2

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  • Hands-On 1/2, QuickRank models usedtar.gz

    Hands-On 1/2, QuickRank models used

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Additional Info
Field Value
Availability On-Line
Group e-Learning
Lesson number 3
Prerequisites See Instructions.txt
Provider Institution Tutorial by HPC Lab, ISTI-CNR, Pisa, Italy
Target users Data Scientists
Thematic Cluster Other
Training material typology Tutorial
system:type TrainingMaterial
Management Info
Field Value
Author MARCO BRAGHIERI
Maintainer BRAGHIERI MARCO
Version 1
Last Updated 12 November 2021, 17:36 (CET)
Created 12 November 2021, 17:27 (CET)