research-article

Learning Online Trends for Interactive Query Auto-Completion

Authors:

Yi ChangAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 29, Issue 11

Pages 2442 - 2454

https://doi.org/10.1109/TKDE.2017.2738639

Published: 01 November 2017 Publication History

Abstract

Query auto-completion (QAC) is widely used by modern search engines to assist users by predicting their intended queries. Most QAC approaches rely on deterministic batch learning algorithms trained from past query log data. However, query popularities keep changing all the time and QAC operates in a real-time scenario where users interact with the search engine continually. So, ideally, QAC must be timely and adaptive enough to reflect time-sensitive changes in an online fashion. Second, due to the vertical position bias, a query suggestion with a higher rank tends to attract more clicks regardless of user’s original intention. Hence, in the long run, it is important to place some lower ranked yet potentially more relevant queries to higher positions to collect more valuable user feedbacks. In order to tackle these issues, we propose to formulate QAC as a ranked Multi-Armed Bandits (MAB) problem which enjoys theoretical soundness. To utilize prior knowledge from query logs, we propose to use Bayesian inference and Thompson Sampling to solve this MAB problem. Extensive experiments on large scale datasets show that our QAC algorithm has the capacity to adaptively learn temporal trends, and outperforms existing QAC algorithms in ranking qualities.

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Cited By

Yi QYang YTang SLiu JWang Y(2024)Effective Generalized Low-Rank Tensor Contextual BanditsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.346978236:12(8051-8065)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3469782
Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28241-6_35
Kropotov IMedlar AGlowacka DDemartini GZuccon GCulpepper JHuang ZTong H(2021)Exploratory Search of GANs with Contextual BanditsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482103(3157-3161)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482103
Show More Cited By

Index Terms

Learning Online Trends for Interactive Query Auto-Completion

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 29, Issue 11

Nov. 2017

255 pages

ISSN:1041-4347

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1041-4347 © 2016 IEEE.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2017

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Cited By

Yi QYang YTang SLiu JWang Y(2024)Effective Generalized Low-Rank Tensor Contextual BanditsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.346978236:12(8051-8065)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3469782
Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28241-6_35
Kropotov IMedlar AGlowacka DDemartini GZuccon GCulpepper JHuang ZTong H(2021)Exploratory Search of GANs with Contextual BanditsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482103(3157-3161)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482103
Yin DTan JZhang ZDeng HHuang SChen JGupta RLiu YShah MRajan STang JPrakash B(2020)Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive ApproachProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403350(2998-3007)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403350

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