Deep Pairwise Learning To Rank For Search Autocomplete
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 11 Aug 2021.
Builds on
5 of 17 references.
References · 5
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2018Personalized neural language models for real-world query auto completioncited by 10
- 2019Personalized Query Auto-Completion Through a Lightweight Representation of the User Contextcited by 4
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.