# Learning Groupwise Scoring Functions Using Deep Neural Networks

**Authors:** Qingyao Ai, Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Marc Najork  
**arXiv:** [1811.04415](https://arxiv.org/abs/1811.04415)  
**Date:** 2018-11-11  
**Summary page:** https://www.summarizepaper.com/en/paper/1811.04415v1/

## Key points

- Paper title: "Learning Groupwise Scoring Functions Using Deep Neural Networks" by Qingyao Ai, Xuanhui Wang, Nadav Golbandi, Michael Bendersky and Marc Najork
- Explores learning-to-rank algorithms
- Highlights the difference between classification/regression settings and ranking settings for document relevance
- Proposes generalized groupwise scoring functions (GSFs) to address limitations of existing pointwise scoring functions
- Utilizes deep neural networks to learn GSFs
- Evaluation on MSLR-WEB30K dataset shows significant performance improvements with GSFs
- Contributes to advancing understanding and application of learning-to-rank algorithms through innovative use of deep neural networks

## Summary

The paper "Learning Groupwise Scoring Functions Using Deep Neural Networks" by Qingyao Ai, Xuanhui Wang, Nadav Golbandi, Michael Bendersky and Marc Najork explores the realm of learning-to-rank algorithms. It highlights the distinction between classification/regression settings and ranking settings in determining document relevance. The authors propose generalized groupwise scoring functions (GSFs) to overcome limitations of existing pointwise scoring functions and leverage deep neural networks to learn GSFs. Evaluation on the MSLR-WEB30K dataset shows significant performance improvements with GSFs. This research contributes to advancing understanding and application of learning-to-rank algorithms through a novel approach using deep neural networks for GSFs.