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To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation

Authors:

Makoto P. Kato,

Katsumi TanakaAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 133 - 142

https://doi.org/10.1145/2835776.2835805

Published: 08 February 2016 Publication History

Abstract

We propose a method of optimizing search result presentation for queries with diverse intents, by selectively presenting query suggestions for leading users to more relevant search results. The optimization is based on a probabilistic model of users who click on query suggestions in accordance with their intents, and modified versions of intent-aware evaluation metrics that take into account the co-occurrence between intents. Showing many query suggestions simply increases a chance to satisfy users with diverse intents in this model, while it in fact requires users to spend additional time for scanning and selecting suggestions, and may result in low satisfaction for some users. Therefore, we measured the loss of time caused by query suggestion presentation by conducting a user study in different settings, and included its negative effects in our optimization problem. Our experiments revealed that the optimization of search result presentation significantly improved that of a single ranked list, and was beneficial especially for patient users. Moreover, experimental results showed that our optimization was effective particularly when intents of a query often co-occur with a small subset of intents.

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Cited By

Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
Aliannejadi MZamani HCrestani FCroft WPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Asking Clarifying Questions in Open-Domain Information-Seeking ConversationsProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331265(475-484)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331265
Radlinski FCraswell NNordlie RPharo NFreund LLarsen BRussel D(2017)A Theoretical Framework for Conversational SearchProceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval10.1145/3020165.3020183(117-126)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3020165.3020183
Show More Cited By

Index Terms

To Suggest, or Not to Suggest for Queries with Diverse Intents: Optimizing Search Result Presentation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

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Published: 08 February 2016

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Ministry of Education, Culture, Sports, Science, and Technology

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WSDM 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
Aliannejadi MZamani HCrestani FCroft WPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Asking Clarifying Questions in Open-Domain Information-Seeking ConversationsProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331265(475-484)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331265
Radlinski FCraswell NNordlie RPharo NFreund LLarsen BRussel D(2017)A Theoretical Framework for Conversational SearchProceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval10.1145/3020165.3020183(117-126)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3020165.3020183
Wu HFang YWu HZhu S(2016)Diversifying trending topic discovery via Semidefinite Programming2016 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2016.7840759(1512-1521)Online publication date: Dec-2016
https://doi.org/10.1109/BigData.2016.7840759

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