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All Journal International Journal of Electrical and Computer Engineering Prosiding SNATIF International Journal of Artificial Intelligence Research AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT Rang Teknik Journal INTEK: Jurnal Penelitian Value: Jurnal Manajemen dan Akuntansi PERSPEKTIF Jurnal Teknologi Elekterika International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Jurnal Litigasi Amsir Prosiding Seminar Riset Mahasiswa
Asriyadi Asriyadi
Fakultas Teknik, Universitas Andalas
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Co-Authors AA Sudharmawan, AA Ade Silvia Handayani Afifah, Luthfia Agung, Muhammad Zakuan Ahamad Rizal Sultan Ahmad Taqwa Ahyar, Muh. Akhriana, Asmah Alwali, Muhammad Ambarwati, Nur Masyita Ayu Andi Wawan Indrawan Aryanti Aryanti Arzuna, Pit BASTIAN, ALVIAN Ciksadan, Ciksadan Ciksadan, Ciksadan Dharma Aryani Dzulqarnain, Andi Emilia Hesti Faris, Fakhri Al Fradesa, Fiza Indah Ayu Lestari, Indah Ayu Irma Salamah Kaila, Afifah Syifah Karović, Sadmir Latif, Asriadi Leni Novianti Lindawati Lindawati M. Ardiansyah Mohd Arifullah Muharram, Syair Muhira Dzar Faraby, Muhira Dzar Mukhlisin Mukhlisin Mustika Mustika Nofriyanti, Duwi Nur Hidayah Nur, Emil Fitra Nur, Fajria Nurhayati Nurhayati Nyayu Latifah Husni, Nyayu Latifah Parenreng, Mardawia Mabe Pranoto, Sarwo Rachmat Ramadhan, Rachmat Rahman, M. Arief Rakhman, M Arief Ramadan, Syahrul Mifta RD Kusumanto Rosita, Ella Saputra, Defri Sawitry, Maryanti Setawati, Elok Suci, Yuni Selvita Suroso Suroso suzan zefi Wahyu Caesarendra Yuniarti Yuniarti