research-article

adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback

Authors:

Carl A. Gunter,

Jiawei HanAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 143 - 152

https://doi.org/10.1145/2766462.2767697

Published: 09 August 2015 Publication History

Abstract

Query auto-completion (QAC) facilitates user query composition by suggesting queries given query prefix inputs. In 2014, global users of Yahoo! Search saved more than 50% keystrokes when submitting English queries by selecting suggestions of QAC. Users' preference of queries can be inferred during user-QAC interactions, such as dwelling on suggestion lists for a long time without selecting query suggestions ranked at the top. However, the wealth of such implicit negative feedback has not been exploited for designing QAC models. Most existing QAC models rank suggested queries for given prefixes based on certain relevance scores.

We take the initiative towards studying implicit negative feed- back during user-QAC interactions. This motivates re-designing QAC in the more general "(static) relevance"(adaptive) implicit negative feedback? framework. We propose a novel adaptive model adaQAC that adapts query auto-completion to users' implicit negative feedback towards unselected query suggestions. We collect user-QAC interaction data and perform large-scale experiments. Empirical results show that implicit negative feedback significantly and consistently boosts the accuracy of the investigated static QAC models that only rely on relevance scores. Our work compellingly makes a key point: QAC should be designed in a more general framework for adapting to implicit negative feedback.

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https://doi.org/10.3390/app13148041
Xiu WWang YXue TZhang KZhang QWu ZYang YZhang GAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts NetworkProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3536320(3355-3359)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3536320
Yi PLi JChoi BBhowmick SXu J(2022)FLAG: Towards Graph Query Autocompletion for Large GraphsData Science and Engineering10.1007/s41019-022-00182-87:2(175-191)Online publication date: 16-Apr-2022
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Index Terms

adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2015

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National Science Foundation

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.3390/app13148041
Xiu WWang YXue TZhang KZhang QWu ZYang YZhang GAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts NetworkProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3536320(3355-3359)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3536320
Yi PLi JChoi BBhowmick SXu J(2022)FLAG: Towards Graph Query Autocompletion for Large GraphsData Science and Engineering10.1007/s41019-022-00182-87:2(175-191)Online publication date: 16-Apr-2022
https://doi.org/10.1007/s41019-022-00182-8
Lehmann FBuschek D(2021)Examining Autocompletion as a Basic Concept for Interaction with Generative AIi-com10.1515/icom-2020-002519:3(251-264)Online publication date: 15-Jan-2021
https://doi.org/10.1515/icom-2020-0025
Hashemi HPappu ATian MChandar PLalmas MCarterette BZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Neural Instant Search for Music and PodcastProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467188(2984-2992)Online publication date: 14-Aug-2021
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Yang YMa BTan JDeng HHuang HZheng Z(2021)FINN: Feedback Interactive Neural Network for Intent RecommendationProceedings of the Web Conference 202110.1145/3442381.3450105(1949-1958)Online publication date: 19-Apr-2021
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Kang YLiu WZhou YLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)QueryBlazer: Efficient Query Autocompletion FrameworkProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441725(1020-1028)Online publication date: 8-Mar-2021
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Huang JWang HFan MZhuo ALi YGupta RLiu YShah MRajan STang JPrakash B(2020)Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu MapsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403318(2677-2685)Online publication date: 23-Aug-2020
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Li YHuang JFan MLei JWang HChen E(2020)Personalized Query Auto-Completion for Large-Scale POI Search at Baidu MapsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/339413719:5(1-16)Online publication date: 18-Jun-2020
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YI PChoi BZhang ZBhowmick SXu J(2020)GFocus: User Focus-based Graph Query AutocompletionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.3002934(1-1)Online publication date: 2020
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