Web-Based Makeup Recommendation System Using Hybrid Filtering
The increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9339 |
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| _version_ | 1849245897416769536 |
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| author | Putu Mia Setya Utami I Nyoman Prayana Trisna Wayan Oger Vihikan |
| author_facet | Putu Mia Setya Utami I Nyoman Prayana Trisna Wayan Oger Vihikan |
| author_sort | Putu Mia Setya Utami |
| collection | DOAJ |
| description | The increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study explores the implementation of Content-Based Filtering (CBF) using TF-IDF and Cosine Similarity, Collaborative Filtering (CF) with Singular Value Decomposition (SVD), and a Hybrid Filtering approach integrating both methods through Weighted Hybrid techniques. The system's performance is evaluated across two user scenarios: new users (without prior ratings) and old users (with rating history). The evaluation method includes Precision, Normalized Discounted Cumulative Gain (NDCG), and accumulation of the best scenario based on user opinion. Results show that Hybrid Filtering outperforms CBF and CF, with notable differences between user groups. For new users, 32% prefer Scenario 1, which emphasizes CBF, achieving 80.8% Precision and 89.73% NDCG. For old users, 23% favor Scenario 2, attaining 83.4% Precision and 90.31% NDCG. |
| format | Article |
| id | doaj-art-e7788383ca8c46e1b102de07e6689b79 |
| institution | Kabale University |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-e7788383ca8c46e1b102de07e6689b792025-08-20T03:58:40ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-019368369210.30871/jaic.v9i3.93396884Web-Based Makeup Recommendation System Using Hybrid FilteringPutu Mia Setya Utami0I Nyoman Prayana Trisna1Wayan Oger Vihikan2Universitas UdayanaUniversitas UdayanaUniversitas UdayanaThe increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study explores the implementation of Content-Based Filtering (CBF) using TF-IDF and Cosine Similarity, Collaborative Filtering (CF) with Singular Value Decomposition (SVD), and a Hybrid Filtering approach integrating both methods through Weighted Hybrid techniques. The system's performance is evaluated across two user scenarios: new users (without prior ratings) and old users (with rating history). The evaluation method includes Precision, Normalized Discounted Cumulative Gain (NDCG), and accumulation of the best scenario based on user opinion. Results show that Hybrid Filtering outperforms CBF and CF, with notable differences between user groups. For new users, 32% prefer Scenario 1, which emphasizes CBF, achieving 80.8% Precision and 89.73% NDCG. For old users, 23% favor Scenario 2, attaining 83.4% Precision and 90.31% NDCG.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9339collaborative filteringcontent-based filteringhybrid filteringrecommendation systemweighted hybrid |
| spellingShingle | Putu Mia Setya Utami I Nyoman Prayana Trisna Wayan Oger Vihikan Web-Based Makeup Recommendation System Using Hybrid Filtering Journal of Applied Informatics and Computing collaborative filtering content-based filtering hybrid filtering recommendation system weighted hybrid |
| title | Web-Based Makeup Recommendation System Using Hybrid Filtering |
| title_full | Web-Based Makeup Recommendation System Using Hybrid Filtering |
| title_fullStr | Web-Based Makeup Recommendation System Using Hybrid Filtering |
| title_full_unstemmed | Web-Based Makeup Recommendation System Using Hybrid Filtering |
| title_short | Web-Based Makeup Recommendation System Using Hybrid Filtering |
| title_sort | web based makeup recommendation system using hybrid filtering |
| topic | collaborative filtering content-based filtering hybrid filtering recommendation system weighted hybrid |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9339 |
| work_keys_str_mv | AT putumiasetyautami webbasedmakeuprecommendationsystemusinghybridfiltering AT inyomanprayanatrisna webbasedmakeuprecommendationsystemusinghybridfiltering AT wayanogervihikan webbasedmakeuprecommendationsystemusinghybridfiltering |