short-paper

Prediction of Good Abandonment Behavior in Mobile Search

Authors:

Dan Wu,

Shutian ZhangAuthors Info & Claims

CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

Pages 407 - 411

https://doi.org/10.1145/3343413.3378007

Published: 14 March 2020 Publication History

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Abstract

Good abandonment behavior means that in a query, the user can obtain the required information directly through the search results without clicking any linked page or reformulating the search query, which is common in mobile search at present. In this paper, a good abandonment prediction model in mobile search was constructed from 5 groups of features: session features, query features, SERP features, mobile touch interaction features and visual attention features. The visual attention features are introduced into good abandonment prediction for the first time and proved to be able to improve the prediction accuracy.

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Cited By

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Hsu CTung HShuai HChang Y(2023)Predicting and Exploring Abandonment Signals in a Banking Task-Oriented Chatbot ServiceInternational Journal of Human–Computer Interaction10.1080/10447318.2023.2282220(1-15)Online publication date: 20-Nov-2023
https://doi.org/10.1080/10447318.2023.2282220
Chen JZhang LLu QLiu HChen S(2023)Predicting information usefulness in health information identification from modal behaviorsInformation Processing & Management10.1016/j.ipm.2022.10322060:2(103220)Online publication date: Mar-2023
https://doi.org/10.1016/j.ipm.2022.103220

Index Terms

Prediction of Good Abandonment Behavior in Mobile Search
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Published In

CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

March 2020

596 pages

ISBN:9781450368926

DOI:10.1145/3343413

General Chairs:
Heather O'Brien
University of British Columbia, Canada
,
Luanne Freund
University of British Columbia, Canada
,
Program Chairs:
Ioannis Arapakis
Telefonica I+D, Spain
,
Orland Hoeber
University of Regina, Canada
,
Irene Lopatovska
Pratt Institute, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2020

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Short-paper

Funding Sources

National Natural Science Foundation of China

Conference

CHIIR '20

Sponsor:

CHIIR '20: Conference on Human Information Interaction and Retrieval

March 14 - 18, 2020

Vancouver BC, Canada

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Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

View all

Hsu CTung HShuai HChang Y(2023)Predicting and Exploring Abandonment Signals in a Banking Task-Oriented Chatbot ServiceInternational Journal of Human–Computer Interaction10.1080/10447318.2023.2282220(1-15)Online publication date: 20-Nov-2023
https://doi.org/10.1080/10447318.2023.2282220
Chen JZhang LLu QLiu HChen S(2023)Predicting information usefulness in health information identification from modal behaviorsInformation Processing & Management10.1016/j.ipm.2022.10322060:2(103220)Online publication date: Mar-2023
https://doi.org/10.1016/j.ipm.2022.103220

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