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Fakultas Ilmu Komputer, Universitas Amikom Yogyakarta
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Journal : Jurnal Teknik Informatika (JUTIF)

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A Hybrid Approach for Recommender Systems Based on Alternating Least Squares and CatBoost Yusfida A'la, Fiddin; Hartatik, Hartatik; Riasti, Berliana Kusuma
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5002

Abstract

This study aims to improve the accuracy of movie rating predictions by applying and combining collaborative filtering and machine learning techniques in a hybrid recommender system. The research utilizes the MovieLens dataset to implement two distinct approaches: the Alternating Least Squares (ALS) matrix factorization model and the CatBoost gradient boosting model. The ALS model is trained to capture latent user–item interactions, while CatBoost leverages nonlinear relationships using user and item features. A simple hybrid strategy averages the predictions from both models to evaluate potential performance gains. Experimental results show that the hybrid approach achieves lower error metrics compared to either model individually, with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values of 0.828 and 0.666, respectively. This demonstrates that combining latent factor models with tree-based learning can effectively reduce prediction errors by exploiting complementary strengths. The novelty of this research lies in its efficient yet effective hybridization strategy that improves recommendation quality without complex ensembling techniques. The findings suggest that even lightweight model fusion can significantly enhance predictive accuracy in recommender systems and may be adapted for other domains where combining linear and nonlinear modeling is beneficial. This research contributes to the field of Informatics and Computer Science by demonstrating that a lightweight hybridization of latent factor models and tree-based learning can significantly improve recommender system accuracy while offering practical implications for real-world digital applications.
Co-Authors Abdillah, Fardan Abdul Aziz Abdullah Abdullah Al Abdul Rohim Ananda, Fadhilah Dwi Andri Syafrianto Andy Supriyadi Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anwar, Ahmad Ari Susanti Arifa Satria Dwi Cahya Audita Nuvriasari Berliana Kusuma Riasti Berliana Kusuma Riasti Budi Warsito Destanul Aulia, Destanul Dhiana Puspitawati Dyah Triwahyuningtyas Ema Utami Fiddin Yusfida Firdaus, Nurul Harliana Harliana Hastari Utama Hilman Haroen I Gusti Ngurah Agung Dewantara Putra Indra Permana, Indra Istiana, Dina Iswahyudi, Didik Jamilah Jamilah Kanoena, Melcior Paitin Kencana, Adi Kresna Khairullah, Irfan Khalil Krisnawati Krisnawati Kurniawan, Aas Andri Kusnawi Kusnawi Lucky Adhikrisna Wirasakti Mar'ati, Fudji Sri Mazidah, Umi Mendrofa, Syah Abadi Muhammad Huzain Munawwaroh, Fabroy Fauziyatul Nuril Huda Nury Yuniasih, Nury Pattimura, Yudha Bagas Purnamasari, Hetty Purwandari, Elce R. Rizal Isnanto Rahayu Lubis Rahayu, Supriati Hardi Ramadhani, Zanuarestu Rasyid, Muhammad Rusdi Rony Permadi Rosyid Rosyid Safira, Livia Seplyana, Desy Setiadi F., A. Farhan Shintya Wulan Safitri Sri Devi Nurhayati Subakti, Agung Subari Subari Sugianto, Efendi Sunatar, Bambang Sutardjo, Agus Syahril Hasan - Politeknik Sains dan Teknologi Wiratama Maluku Utara, Syahril Hasan Taufiq Nashrullah Wicahyono, Agus Wiwi Widayani Yusa, Aldo Yusfida A'la, Fiddin Yusfida, Fiddin