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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
MISSHA Ltd., http://www.missha.ae
- 2.
Amore Pacific Co. Ltd., http://www.amorepacific.com
- 3.
Fujitsu, http://www.fujitsu.com/kr/
- 4.
LG Household & Healthcare Ltd., http://www.lgcare.com
References
Jung KY, Na YZ (2005) Effects of the detail types of ladies wear on the sensibility and emotion. J Korean Soc Cloth Ind 7(2):162–168
Jung KY (2010) Human sensibility ergonomics makeup recommendation system using context sensor information. J Contents Assoc 10(7):23–30
Lee ME, Cho GS (2009) Measurement of human sensation for developing sensible textiles. J Hum Factors Ergon Manuf 19(2):168–176
Jung KY, Lee JH (2004) User preference mining through hybrid collaborative filtering and content-based filtering in recommendation system. IEICE Trans Inf Syst E87-D(12):2781–2790
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. J ACM Trans Inf Syst 22(1):5–53
Jung KY, Na YJ (2004) Developing textile design recommendation system according to customer’s sensibility. J Text Inst 94(1–6):207–216
Kim TH, Yang SB (2005) An improved neighbor selection algorithm in collaborative filtering. IEICE Trans Inf Syst E88-D(5):1072–1076
Kim HN, Jia AT, Haa IA, Joa GS (2010) Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. J Electron Commerce Res Appl 9(1):73–83
Kim TH, Yang SB (2005) An effective recommendation algorithm for clustering-based recommender systems. J Adv Artif Intell 3809:1150–1153
Wang J, de Vries AP, Reinders MJT (2006) A user-item relevance model for log-based collaborative filtering. In: Proceedings of European conference on information retrieval. pp 37–48
Korea Meteorological Administration, http://web.kma.go.kr/eng/
Behrens R (2000) A grammar based model for XML schema integration. In: Proceedings of the British national conference on databases. pp 172–190
Chung KY (2011) Sensibility ergonomics fashion recommendation system using weather webbot. In: Proceedings of the international conference on information science and applications. pp 712–717 (IEEE Computer Society)
Jalali M, Mustapha N, Sulaiman Md N, Mamat A (2010) WebPUM: a web-based recommendation system to predict user future movements. J Expert Syst Appl 37(9):6201–6212
Acknowledgments
This research was supported by the MKE, Korea, under the ITRC support program supervised by the NIPA (NIPA-2011-C1090-1131-0004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media B.V.
About this paper
Cite this paper
Chung, KY., Rim, KW., Lee, JH. (2012). Performance Sensibility Influence of Recommended Makeup Styles. In: Kim, K., Ahn, S. (eds) Proceedings of the International Conference on IT Convergence and Security 2011. Lecture Notes in Electrical Engineering, vol 120. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2911-7_41
Download citation
DOI: https://doi.org/10.1007/978-94-007-2911-7_41
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-2910-0
Online ISBN: 978-94-007-2911-7
eBook Packages: EngineeringEngineering (R0)