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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Mathematical and Fundamental Sciences Communication in Biomathematical Sciences Jurnal Teknik Informatika (JUTIF) Jambura Journal of Biomathematics (JJBM) Makara Journal of Science Journal of Engineering and Technological Sciences
Bevina D Handari
Department Of Mathematics, Universitas Indonesia, Depok 16424, Indonesia
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Mathematics Physics Social Sciences

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Solving sparsity and scalability problems for book recommendations on e-commerce Ichsanudin, Muhammad; Handari, Bevina Desjwiandra; Wijanarko, Bambang Dwi; Hertono, Gatot Fatwanto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4865-4877

Abstract

This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Co-Authors Arthana Islamilova Chukwu, Chidozie W. Cindy Cynthia Denny Riama Silaban Devvi Sarwinda Dipo Aldila Farhana, Nadhilah Hati, Wanda Puspita Hengki Tasman Hertono, Gatot Hertono, Gatot Fatwanto Ichsanudin, Muhammad Joshita Djajadisastra Joshita Djajadisastra, Joshita Khoshnaw, Sarbaz H. A. Mendrofa, Gabriella Aileen Musyaffa, Muhammad Majid Rafi Nugraha, Adhitya Dwi Putri Zahra Kamalia Ramadhan, Derio A. Ridho Kresna Wattimena, Ridho Kresna Sarbaz H.A. Khosnaw Sarwinda, Devvi Shahzad, Muhammad Tamalia, Evllyn Valentino Vito Wijanarko, Bambang Dwi