@inproceedings{singh-etal-2024-dial,
title = "{D}i{AL} : Diversity Aware Listwise Ranking for Query Auto-Complete",
author = "Singh, Sonali and
Farfade, Sachin Sudhakar and
Comar, Prakash Mandayam",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.87",
pages = "1152--1162",
abstract = "Query Auto-Complete (QAC) is an essential search feature that suggests users with a list of potential search keyword completions as they type, enabling them to complete their queries faster. While the QAC systems in eCommerce stores generally use the Learning to Rank (LTR) approach optimized based on customer feedback, it struggles to provide diverse suggestions, leading to repetitive queries and limited navigational suggestions related to product categories, attributes, and brands. This paper proposes a novel DiAL framework that explicitly optimizes for diversity alongside customer feedback signals. It achieves this by leveraging a smooth approximation of the diversity-based metric ($\alpha$NDCG) as a listwise loss function and modifying it to balance relevance and diversity. The proposed approach yielded an improvement of 8.5{\%} in mean reciprocal rank (MRR) and 22.8{\%} in $\alpha$NDCG compared to the pairwise ranking approach on an eCommerce dataset, while meeting the ultra-low latency constraints of QAC systems. In an online experiment, the diversity-aware listwise QAC model resulted in a 0.48{\%} lift in revenue. Furthermore, we replicated the proposed approach on a publicly available search log, demonstrating improvements in both diversity and relevance of the suggested queries.",
}
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<abstract>Query Auto-Complete (QAC) is an essential search feature that suggests users with a list of potential search keyword completions as they type, enabling them to complete their queries faster. While the QAC systems in eCommerce stores generally use the Learning to Rank (LTR) approach optimized based on customer feedback, it struggles to provide diverse suggestions, leading to repetitive queries and limited navigational suggestions related to product categories, attributes, and brands. This paper proposes a novel DiAL framework that explicitly optimizes for diversity alongside customer feedback signals. It achieves this by leveraging a smooth approximation of the diversity-based metric (αNDCG) as a listwise loss function and modifying it to balance relevance and diversity. The proposed approach yielded an improvement of 8.5% in mean reciprocal rank (MRR) and 22.8% in αNDCG compared to the pairwise ranking approach on an eCommerce dataset, while meeting the ultra-low latency constraints of QAC systems. In an online experiment, the diversity-aware listwise QAC model resulted in a 0.48% lift in revenue. Furthermore, we replicated the proposed approach on a publicly available search log, demonstrating improvements in both diversity and relevance of the suggested queries.</abstract>
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%0 Conference Proceedings
%T DiAL : Diversity Aware Listwise Ranking for Query Auto-Complete
%A Singh, Sonali
%A Farfade, Sachin Sudhakar
%A Comar, Prakash Mandayam
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F singh-etal-2024-dial
%X Query Auto-Complete (QAC) is an essential search feature that suggests users with a list of potential search keyword completions as they type, enabling them to complete their queries faster. While the QAC systems in eCommerce stores generally use the Learning to Rank (LTR) approach optimized based on customer feedback, it struggles to provide diverse suggestions, leading to repetitive queries and limited navigational suggestions related to product categories, attributes, and brands. This paper proposes a novel DiAL framework that explicitly optimizes for diversity alongside customer feedback signals. It achieves this by leveraging a smooth approximation of the diversity-based metric (αNDCG) as a listwise loss function and modifying it to balance relevance and diversity. The proposed approach yielded an improvement of 8.5% in mean reciprocal rank (MRR) and 22.8% in αNDCG compared to the pairwise ranking approach on an eCommerce dataset, while meeting the ultra-low latency constraints of QAC systems. In an online experiment, the diversity-aware listwise QAC model resulted in a 0.48% lift in revenue. Furthermore, we replicated the proposed approach on a publicly available search log, demonstrating improvements in both diversity and relevance of the suggested queries.
%U https://aclanthology.org/2024.emnlp-industry.87
%P 1152-1162
Markdown (Informal)
[DiAL : Diversity Aware Listwise Ranking for Query Auto-Complete](https://aclanthology.org/2024.emnlp-industry.87) (Singh et al., EMNLP 2024)
ACL
- Sonali Singh, Sachin Sudhakar Farfade, and Prakash Mandayam Comar. 2024. DiAL : Diversity Aware Listwise Ranking for Query Auto-Complete. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1152–1162, Miami, Florida, US. Association for Computational Linguistics.