Open AccessIntelligent Size Matching Recommender System: Fuzzy Logic Approach in Children Clothing Selection
Author(s) -
Nurashikin Saaludin,
Amna Saad,
Cordelia Mason
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012014
Subject(s) - clothing , matching (statistics) , recommender system , marketing , sizing , computer science , business , advertising , the internet , fuzzy logic , world wide web , artificial intelligence , mathematics , art , statistics , archaeology , history , visual arts
Choosing right-fit clothing is important for children since it is related to the conformity of clothes to the body, especially as we know that children normally engage in active routine activities. During the growth period, children grow-up rapidly in a different pattern. Nowadays, with internet and technology advancement, the online retail business has become the preferred shopping mode for internet-savvy customers, especially in the clothing and textile sector. The recent Coronavirus Pandemic has made online retail a necessity. Therefore, this research focuses on establishing a prototype of the children size matching recommender system, a “sizing advisor” for parents to identify the best clothes fitting which matches the requirement of their children’s body size to the sizing of existing brands. The fuzzy logic approach was applied as a heart of the matching system where the triangular membership function has been used in predicting the suitable clothing size. Nine children aged between 6 years old and 12 years old were selected to test the system. The fit was validated by an expert in sizing. The research aims to provide a size matching system to increase buying satisfaction among parents while shopping for children’s clothes online. The manufacturers, as well as small and medium-sized enterprises (SMEs) which engage in online retailing of children’s clothing, may also benefit from reducing return and thus will help increase sales and profitability.