Application of Content-Based Filtering for Moisturizer
Recommendation System Based on Skin Type Suitability
Dublin Core
Title
Application of Content-Based Filtering for Moisturizer
Recommendation System Based on Skin Type Suitability
Recommendation System Based on Skin Type Suitability
Subject
Content-Based Filtering; Moisturizer; Skin Type; TF-IDF; Cosine Similarity;
Description
Many users face significant challenges when trying to select the most suitable moisturizer for their skin. This difficulty often
arises due to the overwhelming variety of available products on the market, combined with a lack of personalized information that
could guide users toward the best choice. To address this issue, the present study aims to develop a recommendation system based on
the Content-Based Filtering approach, which is specifically designed to align the benefits of moisturizer products with the unique needs
of users' skin types. The data for this study were collected manually from 17 moisturizer products featured on the Sociolla e-commerce
platform. Each product was carefully analyzed according to the descriptive information provided, including the benefits claimed and
the skin types for which the product is recommended. The methodology involved several important steps: preprocessing the text from
product descriptions, applying TF-IDF to assign term weights, and calculating cosine similarity scores between the user’s skin profile
and product attributes. The analysis revealed that products such as D10 and D6, which yielded the highest similarity values, are strongly
aligned with particular skin types. The resulting system demonstrates its ability to generate relevant and personalized product
suggestions without the need for prior user preference data. This study concludes that using descriptive content as the foundation for
recommendation logic can significantly enhance accuracy and targeting. Future enhancements may involve expanding the product
database, integrating user-generated reviews, and leveraging machine learning techniques to produce even more adaptive and
intelligent recommendations.
arises due to the overwhelming variety of available products on the market, combined with a lack of personalized information that
could guide users toward the best choice. To address this issue, the present study aims to develop a recommendation system based on
the Content-Based Filtering approach, which is specifically designed to align the benefits of moisturizer products with the unique needs
of users' skin types. The data for this study were collected manually from 17 moisturizer products featured on the Sociolla e-commerce
platform. Each product was carefully analyzed according to the descriptive information provided, including the benefits claimed and
the skin types for which the product is recommended. The methodology involved several important steps: preprocessing the text from
product descriptions, applying TF-IDF to assign term weights, and calculating cosine similarity scores between the user’s skin profile
and product attributes. The analysis revealed that products such as D10 and D6, which yielded the highest similarity values, are strongly
aligned with particular skin types. The resulting system demonstrates its ability to generate relevant and personalized product
suggestions without the need for prior user preference data. This study concludes that using descriptive content as the foundation for
recommendation logic can significantly enhance accuracy and targeting. Future enhancements may involve expanding the product
database, integrating user-generated reviews, and leveraging machine learning techniques to produce even more adaptive and
intelligent recommendations.
Creator
Muhammad Edi Iswantoa
, Azzahra Putri Latifahb,*
, Andi Nur Rachman b
, Genta Nazwar Tarempab
, Azzahra Putri Latifahb,*
, Andi Nur Rachman b
, Genta Nazwar Tarempab
Source
https://jurnal.unsil.ac.id/index.php/jaisi/article/view/15531/4226
Publisher
https://jurnal.unsil.ac.id/index.php/jaisi/article/view/15531/4226
Date
mei 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Muhammad Edi Iswantoa
, Azzahra Putri Latifahb,*
, Andi Nur Rachman b
, Genta Nazwar Tarempab
, “Application of Content-Based Filtering for Moisturizer
Recommendation System Based on Skin Type Suitability,” Repository Horizon University Indonesia, accessed January 27, 2026, https://repository.horizon.ac.id/items/show/9709.
Recommendation System Based on Skin Type Suitability,” Repository Horizon University Indonesia, accessed January 27, 2026, https://repository.horizon.ac.id/items/show/9709.