Evolutionary programming based recommendation system for online shopping

In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the cloth...

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Published in2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference pp. 1 - 4
Main Authors Jehan Jung, Matsuba, Yuka, Mallipeddi, Rammohan, Funaya, Hiroyuki, Ikeda, Kazushi, Minho Lee
Format Conference Proceeding
LanguageEnglish
Japanese
Published APSIPA 01.10.2013
Subjects
Evolutionary computation
Genetic algorithms
Length measurement
Programming
Sociology
Statistics
Visualization
Online AccessGet full text
DOI10.1109/APSIPA.2013.6694236

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Abstract In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the clothes they like differ from clothes they do not like. Therefore, in the proposed system, human preference is measured from the way the human subjects look at the images of different clothes. In other words, the human preference can be measured by using the fixation count and the fixation length using an eye tracking system. Based on the level of human preference, the evolutionary programming suggests new clothes that close the human preference by operations such as selection and mutation. The proposed recommendation is tested with several human subjects and the experimental results are demonstrated.
AbstractList In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on eye movement analysis. Given a set of images of different clothes, the eye movement patterns of the human subjects while looking at the clothes they like differ from clothes they do not like. Therefore, in the proposed system, human preference is measured from the way the human subjects look at the images of different clothes. In other words, the human preference can be measured by using the fixation count and the fixation length using an eye tracking system. Based on the level of human preference, the evolutionary programming suggests new clothes that close the human preference by operations such as selection and mutation. The proposed recommendation is tested with several human subjects and the experimental results are demonstrated.
Author Mallipeddi, Rammohan
Ikeda, Kazushi
Jehan Jung
Funaya, Hiroyuki
Matsuba, Yuka
Minho Lee
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  givenname: Yuka
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  givenname: Hiroyuki
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  givenname: Kazushi
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  organization: Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Nara, Japan
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  surname: Minho Lee
  fullname: Minho Lee
  email: mholee@knu.ac.kr
  organization: Dept. of Sensor Eng., Kyungpook Nat. Univ., Taegu, South Korea
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Snippet In this paper, we propose an interactive evolutionary programming based recommendation system for online shopping that estimates the human preference based on...
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SubjectTerms Evolutionary computation
Genetic algorithms
Length measurement
Programming
Sociology
Statistics
Visualization
Title Evolutionary programming based recommendation system for online shopping
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