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All Journal Indonesian Journal of Humanities and Social Sciences
Rosmalia, Tria Rizky
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Religion Arts Humanities Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Evaluasi Model Faktor Laten dalam Kondisi Kelangkaan Data: Studi Kasus Rendahnya Pembelian Ulang pada E-Commerce Rosmalia, Tria Rizky; Dhenabayu, Riska; Fazlurrahman, Hujjatullah; Dewi, Renny Sari
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8431

Abstract

The accuracy of recommendation systems is vital for successful personalization in e-commerce. However, the low frequency of repeat purchases creates hight data sparsity, limiting models in capturing user preferences. This study compares two latent factor-based algorithms. Matrix Factorization (MF) and Neural Matrix Factorization (NeuMF), using the Olist transaction dataset through data preparation, k-core filtering, and leave last out splitting. Performance was evaluated using HR@10 and NDCG@10. Results show that MF outperforms NeuMF, achieving HR@10 of 0,057 and NDCG@10 of 0,133. Although NeuMD is more complex and represents a deeper learning-based approach, MF can still be more suitable in certain data conditions, especially when interaction are limited. These findings highlight that simpler models may remain more efficient under sparse data, while NeuMF requires richer interactions histories. The study emphasizes repeat purchase frequency as a key factor in designing adaptive reommendations systems.
Co-Authors Fazlurrahman, Hujjatullah Renny Sari Dewi Riska Dhenabayu