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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Context-Aware Sequential Recommendations with Stacked Recurrent Neural Networks - The World Wide Web Conference. https://dl.acm.org/doi/abs/10.1145/3308558.3313567.
[2]	A Personalized Feedback Mechanism Based on Bounded Confidence Learning to Support Consensus Reaching in Group Decision Making - IEEE Journals & Magazine - IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/8884669.
[3]	Recommender Systems.
[4]	Support, Confidence, Minimum support, Frequent itemset, K-itemset, absolute support in data mining. https://t4tutorials.com/support-confidence-minimum-support-frequent-itemset-in-data-mining/.
[5]	Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction, 30 (1): 127-158, March 2020. ISSN 1573-1391. doi: 10.1007/s11257- 019-09256-1.
[6]	Charu Aggarwal and Philip Yu. Mining Associations with the Collective Strength Approach. Knowledge and Data Engineering, IEEE Transactions on, 13: 863-873, December 2001. doi: 10.1109/69.971183.
[7]	Faisal M. Almutairi, Nicholas D. Sidiropoulos, and George Karypis. Context-Aware Recommendation- Based Learning Analytics Using Tensor and Coupled Matrix Factorization. IEEE Journal of Selected Topics in Signal Processing, 11 (5): 729-741, August 2017. ISSN 1941-0484. doi: 10.1109/JSTSP.2017.2705581.
[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.
[9]	Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. Bias and Debias in Recommender System: A Survey and Future Directions, December 2021.
[10]	Lawson Eng, Jackie Bender, Katrina Hueniken, Shayan Kassirian, Laura Mitchell, Reenika Aggarwal, Chelsea Paulo, Elliot C. Smith, Ilana Geist, Karmugi Balaratnam, Alexander Magony, Mindy Liang, Dongyang Yang, Jennifer M. Jones, M. Catherine Brown, Wei Xu, Samir C. Grover, Shabbir M. H. Alibhai, Geoffrey Liu, and Abha A. Gupta. Age differences in patterns and confidence of using internet and social media for cancer- care among cancer survivors. Journal of Geriatric Oncology, 11 (6): 1011-1019, July 2020. ISSN 1879- 4068. doi: 10.1016/j.jgo.2020.02.011.
[11]	Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based Recommendations with Recurrent Neural Networks, March 2016.
[12]	R. Krishankumar, K. S. Ravichandran, Manish Aggarwal, and Sanjay K. Tyagi. Extended hesitant fuzzy linguistic term set with fuzzy confidence for solving group decision-making problems. Neural Computing and Applications, 32 (7): 2879-2896, April 2020. ISSN 1433- 3058. doi: 10.1007/s00521-019-04275-w.
[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.
[14]	Eduardo Pablo. Neural Networks and Deep Learning, Charu C. Aggarwal. Neural Networks and Deep Learning A Textbook, January 2018.
[15]	Lakshmanan Rakkappan and Vaibhav Rajan. Context- aware sequential recommendations with stacked recurrent neural networks. In The World Wide Web Conference, WWW’19, page 31723178, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450366748. doi: 10.1145/3308558.3313567. URL https://doi.org/10.1145/3308558.3313567.
[16]	Norha M. Villegas, Cristian Sánchez, Javier Díaz-Cely, and Gabriel Tamura. Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems, 140: 173-200, January 2018. ISSN 0950-7051. doi: 10.1016/j.knosys.2017.11.003.

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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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