# Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1811.04415 · HTML: https://mlsanity.com/paper/1811.04415/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 22 of 22)

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- [CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking](https://mlsanity.com/paper/2112.08766/) (2021)
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- [JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System](https://mlsanity.com/paper/2207.13311/) (2022)
- [A Statistical Learning Take on the Concordance Index for Survival Analysis](https://mlsanity.com/paper/2302.12059/) (2023)
- [Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking](https://mlsanity.com/paper/2305.00162/) (2023)
- [Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity](https://mlsanity.com/paper/2308.00177/) (2023)
- [Multimodal Label Relevance Ranking via Reinforcement Learning](https://mlsanity.com/paper/2407.13221/) (2024)
- [Towards Principled Learning for Re-ranking in Recommender Systems](https://mlsanity.com/paper/2504.04188/) (2025)
- [Dialogue-Contextualized Re-ranking for Medical History-Taking](https://mlsanity.com/paper/2304.01974/) (2023)
- [Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](https://mlsanity.com/paper/2406.14004/) (2024)
- [TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model](https://mlsanity.com/paper/2411.12064/) (2024)

## Abstract (arXiv)

While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to univariate scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of other documents in the list. To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list. We refer to this framework as GSFs -- groupwise scoring functions. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using click logs from one of the largest commercial email search engines, as well as a public benchmark dataset. In both cases, GSFs lead to significant performance improvements, especially in the presence of sparse textual features.