Related papers: Deep Pairwise Learning To Rank For Search Autocomplete

Deep Pairwise Learning To Rank For Search Autocomplete

URL: http://arxiv.org/abs/2108.04976v1
Date: Wed, 11 Aug 2021 00:33:18 GMT
Title: Deep Pairwise Learning To Rank For Search Autocomplete
Authors: Kai Yuan, Da Kuang
Abstract summary: We propose a novel context-aware neural network based pairwise ranker to improve Autocomplete ranking. Compared to Lambda 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.
Score: 4.8371021653616975
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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