research-article

Effects of Usage-Based Feedback on Video Retrieval: A Simulation-Based Study

Authors:

Frank Hopfgartner,

Joemon M. Jose,

Pablo CastellsAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 29, Issue 2

Article No.: 11, Pages 1 - 32

https://doi.org/10.1145/1961209.1961214

Published: 01 April 2011 Publication History

Abstract

We present a model for exploiting community-based usage information for video retrieval, where implicit usage information from past users is exploited in order to provide enhanced assistance in video retrieval tasks, and alleviate the effects of the semantic gap problem. We propose a graph-based model for all types of implicit and explicit feedback, in which the relevant usage information is represented. Our model is designed to capture the complex interactions of a user with an interactive video retrieval system, including the representation of sequences of user-system interaction during a search session. Building upon this model, four recommendation strategies are defined and evaluated. An evaluation strategy is proposed based on simulated user actions, which enables the evaluation of our recommendation strategies over a usage information pool obtained from 24 users performing four different TRECVid tasks. Furthermore, the proposed simulation approach is used to simulate usage information pools with different characteristics, with which the recommendation approaches are further evaluated on a larger set of tasks, and their performance is studied with respect to the scalability and quality of the available implicit information.

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Quan YDing JGao CLi NYi LJin DLi Y(2023)Alleviating Video-length Effect for Micro-video RecommendationACM Transactions on Information Systems10.1145/361782642:2(1-24)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3617826
Hu YLiu MSu XGao ZNie L(2021)Video Moment Localization via Deep Cross-Modal HashingIEEE Transactions on Image Processing10.1109/TIP.2021.307386730(4667-4677)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3073867
Jiang BHuang XYang CYuan J(2019)SLTFNet: A spatial and language-temporal tensor fusion network for video moment retrievalInformation Processing & Management10.1016/j.ipm.2019.10210456:6(102104)Online publication date: Nov-2019
https://doi.org/10.1016/j.ipm.2019.102104
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Index Terms

Effects of Usage-Based Feedback on Video Retrieval: A Simulation-Based Study
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 29, Issue 2

April 2011

193 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/1961209

Issue’s Table of Contents

Copyright © 2011 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]

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Publication History

Published: 01 April 2011

Accepted: 01 November 2010

Revised: 01 August 2010

Received: 01 December 2009

Published in TOIS Volume 29, Issue 2

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Regional Government of Madrid
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Cited By

Quan YDing JGao CLi NYi LJin DLi Y(2023)Alleviating Video-length Effect for Micro-video RecommendationACM Transactions on Information Systems10.1145/361782642:2(1-24)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3617826
Hu YLiu MSu XGao ZNie L(2021)Video Moment Localization via Deep Cross-Modal HashingIEEE Transactions on Image Processing10.1109/TIP.2021.307386730(4667-4677)Online publication date: 2021
https://doi.org/10.1109/TIP.2021.3073867
Jiang BHuang XYang CYuan J(2019)SLTFNet: A spatial and language-temporal tensor fusion network for video moment retrievalInformation Processing & Management10.1016/j.ipm.2019.10210456:6(102104)Online publication date: Nov-2019
https://doi.org/10.1016/j.ipm.2019.102104
Liu MWang XNie LHe XChen BChua TCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Attentive Moment Retrieval in VideosThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210003(15-24)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210003
Plumbaum TKlein-Ellinghaus FReeske APelz KHopfgartner F(2015)Health Assistance for ImmigrantsSmart Information Systems10.1007/978-3-319-14178-7_3(75-97)Online publication date: 15-Jan-2015
https://doi.org/10.1007/978-3-319-14178-7_3
Halvey MVallet DHannah DJose J(2014)Supporting exploratory video retrieval tasks with grouping and recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2014.06.00450:6(876-898)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1016/j.ipm.2014.06.004
Hopfgartner FJose J(2014)An experimental evaluation of ontology-based user profilesMultimedia Tools and Applications10.1007/s11042-012-1254-273:2(1029-1051)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1007/s11042-012-1254-2
L’Huillier GVelásquez J(2013)Web Usage Data Pre-processingAdvanced Techniques in Web Intelligence-210.1007/978-3-642-33326-2_2(11-34)Online publication date: 2013
https://doi.org/10.1007/978-3-642-33326-2_2

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