Evaluasi Komparatif Algoritma Ensemble Learning pada Learning to Rank untuk Rekomendasi Produk berdasarkan data Transaksi E-Commerce
Keywords:
Learning to Rank, XGBoost, LightGBM, CatBoost, Rekomendasi Produk, NDCG, E-CommerceAbstract
Learning to Rank (LTR) merupakan pendekatan machine learning yang telah menjadi inti dari sistem rekomendasi produk modern dalam industri e-commerce. Penelitian ini bertujuan membandingkan performa tiga algoritma gradient boosting, yaitu XGBoost, LightGBM, dan CatBoost, yang diterapkan pada tugas LTR menggunakan Instacart Online Grocery Shopping Dataset 2017. Dataset tersebut memuat lebih dari 3 juta pesanan dari lebih dari 200.000 pengguna anonim. Feature engineering dilakukan dengan menghitung statistik agregasi pada level pengguna dan level produk berdasarkan data transaksi historis. Pembagian data dilakukan berdasarkan pengguna untuk mencegah kebocoran informasi antara set pelatihan dan pengujian. Setiap algoritma dilatih menggunakan objektif ranking listwise atau pairwise bawaan masing-masing, dan dievaluasi menggunakan metrik NDCG@5, MAP@5, dan Mean Reciprocal Rank (MRR). Hasil eksperimen menunjukkan bahwa CatBoost mencapai NDCG@5 tertinggi (0,8036) dan MAP@5 tertinggi (0,8256), sementara XGBoost dan LightGBM menghasilkan performa yang kompetitif dan hampir identik. Ketiga model secara signifikan melampaui baseline Random Forest. Temuan ini menunjukkan bahwa algoritma gradient boosting sangat efektif untuk LTR dalam skenario rekomendasi ecommerce dan memberikan panduan praktis dalam pemilihan model ranking untuk sistem produksi.
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