# Deep Pairwise Learning To Rank For Search Autocomplete

arXiv: https://arxiv.org/abs/2108.04976 · HTML: https://mlsanity.com/paper/2108.04976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Personalized neural language models for real-world query auto completion](https://mlsanity.com/paper/1804.06439/) (2018)
- [Personalized Query Auto-Completion Through a Lightweight Representation of the User Context](https://mlsanity.com/paper/1905.01386/) (2019)

## Built on by (most cited first, 2 of 2)

- [Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries](https://mlsanity.com/paper/2308.02055/) (2023)
- [Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment](https://mlsanity.com/paper/2602.01023/) (2026)

## Abstract (arXiv)

Autocomplete (a.k.a "Query Auto-Completion", "AC") suggests full queries based on a prefix typed by customer. Autocomplete has been a core feature of commercial search engine. In this paper, we propose a novel context-aware neural network based pairwise ranker (DeepPLTR) to improve AC ranking, DeepPLTR leverages contextual and behavioral features to rank queries by minimizing a pairwise loss, based on a fully-connected neural network structure. Compared to LambdaMART ranker, DeepPLTR shows +3.90% MeanReciprocalRank (MRR) lift in offline evaluation, and yielded +0.06% (p < 0.1) Gross Merchandise Value (GMV) lift in an Amazon's online A/B experiment.