Online Transaction Shopping Items Basket Recommender Systems

Christodoulos Asiminidis

doi:doi:10.11648/j.mlr.20220702.11

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Online Transaction Shopping Items Basket Recommender Systems

Christodoulos Asiminidis

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

Received: 12 July 2022 Accepted: 28 July 2022 Published: 25 August 2022

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Abstract

Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.

Published in	Machine Learning Research (Volume 7, Issue 2)
DOI	10.11648/j.mlr.20220702.11
Page(s)	15-17
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), 2022. Published by Science Publishing Group

Keywords

Recommender Systems, Transactions, E-shop, Website, Online Transaction Basket Metric, Mathematical Functions

References

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[8]	Rocío Cañamares and Pablo Castells. On Target Item SamplinginOfflineRecommenderSystemEvaluation. In Fourteenth ACM Conference on Recommender Systems, pages 259-268. Association for Computing Machinery, New York, NY, USA, September 2020. ISBN 978-1- 4503-7583-2.
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[13]	Venkatesan M and Thangadurai K. History and Overview of the Recommender Systems. https://www.igi- global.com/chapter/history-and-overview-of-the- recommender-systems/www.igi-global.com/chapter/ history-and-overview-of-the-recommender-systems/ 159496, 2017.
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APA Style

Christodoulos Asiminidis. (2022). Online Transaction Shopping Items Basket Recommender Systems. Machine Learning Research, 7(2), 15-17. https://doi.org/10.11648/j.mlr.20220702.11

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Christodoulos Asiminidis. Online Transaction Shopping Items Basket Recommender Systems. Mach. Learn. Res. 2022, 7(2), 15-17. doi: 10.11648/j.mlr.20220702.11

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AMA Style

Christodoulos Asiminidis. Online Transaction Shopping Items Basket Recommender Systems. Mach Learn Res. 2022;7(2):15-17. doi: 10.11648/j.mlr.20220702.11

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@article{10.11648/j.mlr.20220702.11,
  author = {Christodoulos Asiminidis},
  title = {Online Transaction Shopping Items Basket Recommender Systems},
  journal = {Machine Learning Research},
  volume = {7},
  number = {2},
  pages = {15-17},
  doi = {10.11648/j.mlr.20220702.11},
  url = {https://doi.org/10.11648/j.mlr.20220702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20220702.11},
  abstract = {Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.},
 year = {2022}
}

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TY  - JOUR
T1  - Online Transaction Shopping Items Basket Recommender Systems
AU  - Christodoulos Asiminidis
Y1  - 2022/08/25
PY  - 2022
N1  - https://doi.org/10.11648/j.mlr.20220702.11
DO  - 10.11648/j.mlr.20220702.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 15
EP  - 17
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20220702.11
AB  - Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.
VL  - 7
IS  - 2
ER  -

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Author Information

Christodoulos Asiminidis

Department of Computer Science, Imperial College London, London, UK

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APA Style

Christodoulos Asiminidis. (2022). Online Transaction Shopping Items Basket Recommender Systems. Machine Learning Research, 7(2), 15-17. https://doi.org/10.11648/j.mlr.20220702.11

Copy | Download

ACS Style

Christodoulos Asiminidis. Online Transaction Shopping Items Basket Recommender Systems. Mach. Learn. Res. 2022, 7(2), 15-17. doi: 10.11648/j.mlr.20220702.11

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AMA Style

Christodoulos Asiminidis. Online Transaction Shopping Items Basket Recommender Systems. Mach Learn Res. 2022;7(2):15-17. doi: 10.11648/j.mlr.20220702.11

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@article{10.11648/j.mlr.20220702.11,
  author = {Christodoulos Asiminidis},
  title = {Online Transaction Shopping Items Basket Recommender Systems},
  journal = {Machine Learning Research},
  volume = {7},
  number = {2},
  pages = {15-17},
  doi = {10.11648/j.mlr.20220702.11},
  url = {https://doi.org/10.11648/j.mlr.20220702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20220702.11},
  abstract = {Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Online Transaction Shopping Items Basket Recommender Systems
AU  - Christodoulos Asiminidis
Y1  - 2022/08/25
PY  - 2022
N1  - https://doi.org/10.11648/j.mlr.20220702.11
DO  - 10.11648/j.mlr.20220702.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 15
EP  - 17
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20220702.11
AB  - Recommender systems firstly appeared in the early of 1990s. Since then, even more scientists explore the world of recommender system. Nowadays, recommender system can be found on every big website’s company such as Amazon, Netflix and Ebay. In this research work, we focus on state-of-the-art metric that involves recommender systems. The particular metric is the exploration and exploitation of a mathematical function described as weighted support and confidence. The particular metrics have as a primary goal the implementation of them into a recommender system which takes as items into a recommender systems whose similarity metric takes into consideration. According to the aforementioned metrics, results have shown that the mathematical methods will bring important outcomes in data sets such as the ones that can be found on e-shop online websites in recommender systems. This work is part of examination of state-of-the-art mathematical model applied in online stores.
VL  - 7
IS  - 2
ER  -

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