Skincare Product Recommendation System Using Hybrid Ensemble Learning
DOI:
https://doi.org/10.30871/jaic.v10i2.10461Keywords:
Ensemble Learning, Hybrid recommender system, Random Forest, TF-IDF, SVD Matrix FactorizationAbstract
The global beauty and personal care products market reached USD 557.24 billion in 2023 and is projected to reach USD 937.13 billion by 2030, growing at a CAGR of 7.7%. This growth is driven by increasing consumer awareness of personal appearance and the proliferation of e-commerce platforms, particularly in the Asia-Pacific region. However, the wide variety of skincare products creates information overload, making it difficult for consumers to identify products that suit their specific needs. Traditional recommendation systems generally rely on a single approach, either content-based filtering, which struggles to capture user preferences, or collaborative filtering, which faces cold-start and data sparsity issues. This study proposes a hybrid ensemble learning approach that integrates three complementary techniques: (1) TF-IDF content-based filtering to analyze product similarities based on brand, category, and product attributes; (2) SVD matrix factorization collaborative filtering to capture latent patterns of user-product interactions through synthetic user data generation; and (3) Random Forest as a meta-learner to intelligently combine the outputs of the two methods. The proposed system was evaluated using a dataset of more than 7,500 skincare products from Sociolla via the Kaggle repository, covering more than 300 brands with detailed product attributes including ratings, reviews, prices, and customer engagement metrics. The hybrid ensemble approach showed strong predictive performance with an R² score of 0.830, explaining 83.0% of the variance in product ratings. The system successfully recommended five products from five different brands (Biyu, True-to-skin, Jacquelle, The-aubree, and Biore) with ensemble scores ranging from 0.872 to 0.996, demonstrating cross-brand recommendation capabilities. Feature importance analysis shows relatively equal contributions from log_wishlist (29.7%), brand_encoded (25.6%), log_reviews (24.1%), and category_encoded (20.6%). These findings indicate that this hybrid approach effectively overcomes the limitations of single-method recommendation systems and can be adapted to various e-commerce product categories, providing a more relevant and personalized shopping experience for consumers.
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