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Department of Computer Science, Imperial College London, London, UK
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.
Recommender Systems, Transactions, E-shop, Website, Online Transaction Basket Metric, Mathematical Functions
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
Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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