Skin Care product Recommendation
Abstract
In today’s era of personalized wellness and digital
convenience, recommending appropriate skincare
products tailored to individual needs has become
both a scientific and technological challenge. This
project focuses on the development of an intelligent
skin care product recommendation system that
leverages user-specific data such as skin type,
concerns (e.g., acne, dryness, sensitivity),
environmental conditions, and lifestyle habits. By
utilizing machine learning algorithms and
dermatological databases, the system analyzes
input parameters to suggest the most suitable
skincare routines and products. It aims to address
common issues of product mismatch and allergic
reactions by offering recommendations grounded in
evidence-based skin science. The integration of AI
not only enhances the accuracy of suggestions but
also provides users with real-time feedback and
updates based on seasonal or behavioral changes.
This project holds significant potential in
transforming the skincare industry by enabling
personalized, data-driven decisions that improve
skin health and user satisfaction.
Downloads
References
L., Xu, Q., & Zhang, Z. (2019). A
Personalized Skincare System Based on
Machine Learning for Recommendation.
Journal of AI & Machine Learning, 23(5),
344-355.
2. Patel, V., & Sharma, P. (2020). Skin Care
Product Recommendation Using Neural
Networks and Natural Language
Processing. International Journal of
Computing and Digital Systems, 9(3), 112-
125.
3. Lee, H., & Song, H. (2018). Personalized
Skin Care Product Recommendations
Using Collaborative Filtering and Deep
Learning. SkinTech Journal of AI, 14(2),
25-39.
4. Sarkar, S., & Gupta, A. (2021). Skin
Type and Concern Detection Using
Convolutional Neural Networks for
Effective Skincare Recommendations.Journal of Dermatology and AI, 32(1), 58-
67.
5. Kim, M., & Lee, J. (2020). Machine
Learning-Based Recommendation System
for Skincare Routine Personalization.
International Journal of Artificial
Intelligence, 16(4), 232-245.
6. Bello, J., & Tan, S. (2017). Personalizing
Skin Care Product Choices Using Deep
Learning Algorithms. Artificial
Intelligence Review, 45(2), 77-89.
7. Jones, K., & Patel, R. (2019). Evaluating
the Impact of Product Ingredients in
Personalized Skin Care
Recommendations. Journal of Consumer
Dermatology, 28(6), 245-256.
8. Xu, L., Zhang, X., & Li, Y. (2021).
Recommendation of Cosmetics Using
Machine Learning and Skin Type
Analysis. Journal of Cosmetics Science,
41(3), 112-124.
9. Sharma, P., & Agarwal, S. (2018). Real-
Time Skin Moisture Detection Using
Image Recognition for Personalized Skin
Care Suggestions. IEEE Transactions on
Pattern Recognition, 36(1), 89-101.
10. Choi, M., & Park, H. (2017). Advanced
Recommendation System for Cosmetics:
A Hybrid Model Based on Machine
Learning and Expert Knowledge. Journal
of Smart Computing, 19(4), 76-91.
11. Chang, J., & Wang, T. (2020).
Personalized Skincare Recommendation
Engine Based on Collaborative Filtering
and Expert Input. International Journal of
Personalization Technology, 12(3), 102-
115.
12. Duan, Z., & Zhang, H. (2019). Smart
Skincare Product Recommendation:
Leveraging Data Mining and User
Behavior. Expert Systems with
Applications, 137, 323-337.
13. Yang, F., & Liu, Q. (2020). An Intelligent
Skincare Product Recommendation
Framework Using Natural Language
Processing. International Journal of
Machine Learning and AI, 44(2), 62-74.
14. Miller, J., & Green, T. (2018). Skin
Health Data Analytics for Personalized
Product Recommendations. Journal of
Health Informatics, 31(5), 234-248.
15. Chen, Y., & Ma, L. (2017). Data-Driven
Skincare Product Recommender Using AI
and Consumer Preferences. Journal of
Consumer Technology, 21(8), 401-413.
16. Chung, S., & Lee, J. (2020). AI-Driven
Personalized Skincare Recommendation
Systems: Leveraging Skin Type and
Environment. Artificial Intelligence in
Dermatology, 6(3), 101-113.
17. Wang, Q., & Kim, H. (2019). Utilizing
Deep Learning for Tailored Skin Care
Product Suggestions: A Review of Current
Methods. Journal of AI and Health, 18(2),
65-78.
