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All Journal Journal of Intelligent Computing, Systems, and Applications (JICSA)
Bryan Simamora
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The Comparative Analysis of Item-Based Collaborative Filtering and Alternating Least Squares on Implicit Feedback Recommender Systems Dolok Butarbutar; Brain Sitorus; Krisnia Siahaan; Bryan Simamora; Nikita Simanjuntak
Journal of Intelligent Computing, Systems, and Applications (JICSA) Vol. 1 No. 02 (2026): March 2026
Publisher : Penerbit Institut Teknologi Del

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54074/jicsa.v1i02.21

Abstract

This study addresses the challenge of predicting user preferences using implicit feedback data. We compared Popularity-Based and Item-Based Collaborative Filtering (IBCF) baselines against the Alternating Least Squares (ALS) method, evaluating performance via Mean Average Precision at 10 (MAP@10). Results show that ALS- Tuned achieved the highest score (MAP@10 = 0.012788), showing best performance compared to the baseline model. This indicates that hyperparameter tuning affects the most in model performance.
Co-Authors Brain Sitorus Dolok Butarbutar Krisnia Siahaan Nikita Simanjuntak