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Selectively Personalizing Query Auto-Completion

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SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 993 - 996

https://doi.org/10.1145/2911451.2914686

Published: 07 July 2016 Publication History

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Abstract

Query auto-completion (QAC) is being used by many of today's search engines. It helps searchers formulate queries by providing a list of query completions after entering an initial prefix of a query. To cater for a user's specific information needs, personalized QAC strategies use a searcher's search history and their profile. Is personalization consistently effective in different search contexts?

We study the QAC problem by selectively personalizing the query completion list. Based on a lenient personalized QAC strategy that encodes the ranking signal as a trade-off between query popularity and search context, we propose a model for selectively personalizing query auto-completion (SP-QAC) to study this trade-off. We predict effective trade-offs based on a regression model, where the typed query prefix, clicked documents and preceding queries in the same session are used to weigh personalization in QAC. Experiments on the AOL query log show the SP-QAC model can significantly outperform a state-of-the-art personalized QAC approach.

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Cited By

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Chen CLee SSundar S(2023)Interpassivity instead of interactivity? The uses and gratifications of automated featuresBehaviour & Information Technology10.1080/0144929X.2023.218417443:4(717-735)Online publication date: 2-Mar-2023
https://doi.org/10.1080/0144929X.2023.2184174
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
Vuong TAndolina SJacucci GRuotsalo T(2021)Spoken Conversational Context Improves Query Auto-completion in Web SearchACM Transactions on Information Systems10.1145/344787539:3(1-32)Online publication date: 5-May-2021
https://dl.acm.org/doi/10.1145/3447875
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Index Terms

Selectively Personalizing Query Auto-Completion
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

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Published: 07 July 2016

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SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

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

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Chen CLee SSundar S(2023)Interpassivity instead of interactivity? The uses and gratifications of automated featuresBehaviour & Information Technology10.1080/0144929X.2023.218417443:4(717-735)Online publication date: 2-Mar-2023
https://doi.org/10.1080/0144929X.2023.2184174
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
Vuong TAndolina SJacucci GRuotsalo T(2021)Spoken Conversational Context Improves Query Auto-completion in Web SearchACM Transactions on Information Systems10.1145/344787539:3(1-32)Online publication date: 5-May-2021
https://dl.acm.org/doi/10.1145/3447875
Torbati GYates AWeikum G(2021)You Get What You Chat: Using Conversations to Personalize Search-Based RecommendationsAdvances in Information Retrieval10.1007/978-3-030-72113-8_14(207-223)Online publication date: 28-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72113-8_14
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
https://dl.acm.org/doi/10.1145/3394486.3403318
Torbati GYates AWeikum GO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)Personalized Entity Search by Sparse and Scrutable User ProfilesProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378011(427-431)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378011
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Xie XMao JLiu Yde Rijke MAi QHuang YZhang MMa SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Improving Web Image Search with Contextual InformationProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3358011(1683-1692)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3358011
Ramachandran LMurthy UBogers TSaid ABrusilovsky PTikk D(2019)GhostingProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346995(196-200)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3346995
Qin JXiao CHu SZhang JWang WIshikawa YTsuda KSadakane K(2019)Efficient query autocompletion with edit distance-based error toleranceThe VLDB Journal10.1007/s00778-019-00595-429:4(919-943)Online publication date: 14-Dec-2019
https://doi.org/10.1007/s00778-019-00595-4
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Time-sensitive Personalized Query Auto-Completion

Diversifying Query Auto-Completion

Learning to personalize query auto-completion

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