Abstract
In this paper, we proposed a performance sensibility influence of the recommended makeup styles. Development of the facial makeup style recommendation system used a user interface and collaborative filtering for the makeup styles to satisfy the user’s needs. Collaborative filtering was adopted to recommend makeup styles of interest for users based on the predictive relationship discovered between the current user and other previous users. We used makeup styles in the survey questionnaire. 1,630,084 ratings were collected from 978 users. The pictures of makeup style details, such as foundation, color lens, eye shadow, blusher, eyelash, lipstick, hairstyle, hairpin, necklace, earring, and hair length were evaluated in terms of sensibility. The data were analyzed by SPSS using ANOVA and factor analysis to discover the most effective types of details from the consumer’s sensibility viewpoint. Sensibility was composed of three concepts: contemporary, mature and individual. The details of makeup styles were positioned in 3D-concept space to relate each type of detail to the makeup concept regarding a woman’s cosmetics.KeywordsMakeup styleRecommender systemCollaborative filteringCosmeticSensibility
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