Automatic Indexing of Digital Objects Through Learning from User Data

Clement Leung; Yuanxi Li

doi:doi:10.11648/j.mlr.20220702.12

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Automatic Indexing of Digital Objects Through Learning from User Data

Clement Leung, Yuanxi Li

Published in Machine Learning Research (Volume 7, Issue 2)

Received: 17 December 2022 Accepted: 12 January 2023 Published: 31 January 2023

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Abstract

Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.

Published in	Machine Learning Research (Volume 7, Issue 2)
DOI	10.11648/j.mlr.20220702.12
Page(s)	18-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Autonomous Agent, Digital Data Objects, Index Generation, Multimedia Information Search, Probability Generating Function, Reinforcement Learning, Stochastic Modelling

References

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APA Style

Clement Leung, Yuanxi Li. (2023). Automatic Indexing of Digital Objects Through Learning from User Data. Machine Learning Research, 7(2), 18-23. https://doi.org/10.11648/j.mlr.20220702.12

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ACS Style

Clement Leung; Yuanxi Li. Automatic Indexing of Digital Objects Through Learning from User Data. Mach. Learn. Res. 2023, 7(2), 18-23. doi: 10.11648/j.mlr.20220702.12

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AMA Style

Clement Leung, Yuanxi Li. Automatic Indexing of Digital Objects Through Learning from User Data. Mach Learn Res. 2023;7(2):18-23. doi: 10.11648/j.mlr.20220702.12

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@article{10.11648/j.mlr.20220702.12,
  author = {Clement Leung and Yuanxi Li},
  title = {Automatic Indexing of Digital Objects Through Learning from User Data},
  journal = {Machine Learning Research},
  volume = {7},
  number = {2},
  pages = {18-23},
  doi = {10.11648/j.mlr.20220702.12},
  url = {https://doi.org/10.11648/j.mlr.20220702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20220702.12},
  abstract = {Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.},
 year = {2023}
}

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TY  - JOUR
T1  - Automatic Indexing of Digital Objects Through Learning from User Data
AU  - Clement Leung
AU  - Yuanxi Li
Y1  - 2023/01/31
PY  - 2023
N1  - https://doi.org/10.11648/j.mlr.20220702.12
DO  - 10.11648/j.mlr.20220702.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20220702.12
AB  - Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.
VL  - 7
IS  - 2
ER  -

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Author Information

Clement Leung

School of Science and Engineering and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong, Shenzhen, China
Yuanxi Li

Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

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APA Style

Clement Leung, Yuanxi Li. (2023). Automatic Indexing of Digital Objects Through Learning from User Data. Machine Learning Research, 7(2), 18-23. https://doi.org/10.11648/j.mlr.20220702.12

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ACS Style

Clement Leung; Yuanxi Li. Automatic Indexing of Digital Objects Through Learning from User Data. Mach. Learn. Res. 2023, 7(2), 18-23. doi: 10.11648/j.mlr.20220702.12

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AMA Style

Clement Leung, Yuanxi Li. Automatic Indexing of Digital Objects Through Learning from User Data. Mach Learn Res. 2023;7(2):18-23. doi: 10.11648/j.mlr.20220702.12

Copy | Download

@article{10.11648/j.mlr.20220702.12,
  author = {Clement Leung and Yuanxi Li},
  title = {Automatic Indexing of Digital Objects Through Learning from User Data},
  journal = {Machine Learning Research},
  volume = {7},
  number = {2},
  pages = {18-23},
  doi = {10.11648/j.mlr.20220702.12},
  url = {https://doi.org/10.11648/j.mlr.20220702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20220702.12},
  abstract = {Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Automatic Indexing of Digital Objects Through Learning from User Data
AU  - Clement Leung
AU  - Yuanxi Li
Y1  - 2023/01/31
PY  - 2023
N1  - https://doi.org/10.11648/j.mlr.20220702.12
DO  - 10.11648/j.mlr.20220702.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20220702.12
AB  - Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism. Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories. In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions. Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations. By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage. The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable. The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum. Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.
VL  - 7
IS  - 2
ER  -

Copy | Download