Filtering out the irrelevant features to enhance the generaliza0on performance of the learned model. • Iden+fying key features also helps to reverse ...

This tutorial discusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time.

This tutorial dis- cusses the recent solutions that allow to build an effective ranking model that satisfies temporal budget constrains at evaluation time.

At the end of the day you'll be able to train a high quality ranking model, and to exploit SoA tools and techniques to reduce its computational cost up to 18x !

In the last years, Learning to Rank (LtR) had a significant influence on several tasks in the Information Retrieval field, with large research efforts ...

This tutorial provides an 'Introduction to Learning to Rank' and focuses on 'Dealing with the Efficiency/Effectiveness trade-off in Web Search'.

In this work, we show that it is possible to retain the benefits of transformer-based rerankers in a multi-stage reranking pipeline.

Efficiency/Effectiveness. Trade-offs in Learning to Rank. Tutorial @ ICTIR 2017. Claudio Lucchese. Ca' Foscari University of Venice. Venice, Italy. Franco Maria ...

It is well known that rerankers built on pre- trained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks.

By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps ...