Intelligent Recommender System for online clothing optimization of casual clothing: An approach from the Competitive Imperialist Algorithm

Authors

  • Adrián Loera
  • Alberto Ochoa-Zezatti
  • Jaime Sánchez
  • Inés Borunda
  • José Mejia

Keywords:

Imperialist competitive algorithm, Evolutionary algorithms, Intelligent Recommender system, Case-based reasoning

Abstract

The future state of the companies that offer online services is of great importance for the professionals of the stock market. According to the efficient theory of the market, it is impossible to adequately predict suitable suggestions to the clients considering the historical reference data. A precise prediction offers companies greater credibility, as well as an increase in their sales. The present research demonstrates the resolution of a problem based on the modeling of the online shopping process, selecting the garments that best suit a specific profile. The objective of this research is based on obtaining a recommendation model that allows customizing clothing shopping suggestions online. For this, the Competitive Imperialist Algorithm (ICA) is implemented, analyzing 300 in-stances of garments from a Database provided by the University of Hong Kong. As a result, specific purchase profiles are obtained using our bioinspired algorithm verified through Case-Based Reasoning (CBR), where in this process we will be able to solve the new problems based on the solutions of previous problems to improve the shopping experience in line of users, allowing the adaptation of multiple dynamic environments.

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Published

2019-07-01

How to Cite

Adrián Loera, Alberto Ochoa-Zezatti, Jaime Sánchez, Inés Borunda, & José Mejia. (2019). Intelligent Recommender System for online clothing optimization of casual clothing: An approach from the Competitive Imperialist Algorithm. Journal of Network and Innovative Computing, 7, 7. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/138

Issue

Vol. 7 (2019)

Section

Original Article

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