short-paper

Personalised Search Time Prediction using Markov Chains

Authors:

Leif AzzopardiAuthors Info & Claims

ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval

Pages 237 - 240

https://doi.org/10.1145/3121050.3121085

Published: 01 October 2017 Publication History

Abstract

For improving the effectiveness of Interactive Information Retrieval (IIR), a system should minimise the search time by guiding the user appropriately. As a prerequisite, in any search situation, the system must be able to estimate the time the user will need for finding the next relevant document. In this paper, we show how Markov models derived from search logs can be used for predicting search times, and describe a method for evaluating these predictions. For personalising the predictions based upon a few user events observed, we devise appropriate parameter estimation methods. Our experimental results show that by observing users for only 100 seconds, the personalised predictions are already significantly better than global predictions.

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Cited By

Liu JShah CAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)Leveraging user interaction signals and task state information in adaptively optimizing usefulness-oriented search sessionsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3530926(1-11)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3530926
Amezaga Hechavarria AShafiq M(2022)A modified attention mechanism powered by Bayesian Network for user activity analysis and predictionData & Knowledge Engineering10.1016/j.datak.2022.102034140:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.datak.2022.102034
Zerhoudi SGranitzer MSeifert CSchloetterer J(2022)Evaluating Simulated User Interaction and Search BehaviourAdvances in Information Retrieval10.1007/978-3-030-99739-7_28(240-247)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_28
Show More Cited By

Index Terms

Personalised Search Time Prediction using Markov Chains
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
2. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Published In

cover image ACM Conferences

ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval

October 2017

348 pages

ISBN:9781450344906

DOI:10.1145/3121050

General Chairs:
Jaap Kamps
University of Amsterdam, The Netherlands
,
Evangelos Kanoulas
University of Amsterdam, The Netherlands
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Program Chairs:
Hui Fang
University of Delaware, USA
,
Emine Yilmaz
University College London, UK

Copyright © 2017 ACM.

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]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2017

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Author Tags

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Short-paper

Funding Sources

Deutsche Forschungsgemeinschaft
Evaluating Information Access Systems (ESF Research Networking Program)
Engineering and Physical Sciences Research Council

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ICTIR '17

Sponsor:

SIGIR

ICTIR '17: ACM SIGIR International Conference on the Theory of Information Retrieval

October 1 - 4, 2017

Amsterdam, The Netherlands

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ICTIR '17 Paper Acceptance Rate 27 of 54 submissions, 50%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Cited By

Liu JShah CAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)Leveraging user interaction signals and task state information in adaptively optimizing usefulness-oriented search sessionsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3530926(1-11)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3530926
Amezaga Hechavarria AShafiq M(2022)A modified attention mechanism powered by Bayesian Network for user activity analysis and predictionData & Knowledge Engineering10.1016/j.datak.2022.102034140:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.datak.2022.102034
Zerhoudi SGranitzer MSeifert CSchloetterer J(2022)Evaluating Simulated User Interaction and Search BehaviourAdvances in Information Retrieval10.1007/978-3-030-99739-7_28(240-247)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_28
Hechavarria AShafiq M(2021)Modeling and Predicting Online Learning Activities of Students: An HMM-LSTM based Hybrid Solution2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00114(682-687)Online publication date: Dec-2021
https://doi.org/10.1109/ICMLA52953.2021.00114
Aker AFuhr N(2018)The Information Retrieval Group at the University of Duisburg-EssenDatenbank-Spektrum10.1007/s13222-018-0290-018:2(113-119)Online publication date: 3-Jul-2018
https://doi.org/10.1007/s13222-018-0290-0
Kacem AMayr P(2018)Analysis of search stratagem utilisationScientometrics10.1007/s11192-018-2821-8116:2(1383-1400)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11192-018-2821-8
Tran VFuhr N(2018)Personalised Session Difficulty Prediction in an Online Academic Search EngineDigital Libraries for Open Knowledge10.1007/978-3-030-00066-0_15(174-185)Online publication date: 5-Sep-2018
https://doi.org/10.1007/978-3-030-00066-0_15

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