The Comparative Analysis of Item-Based Collaborative Filtering and Alternating Least Squares on Implicit Feedback Recommender Systems

Dolok Butarbutar (IT Del)
Brain Sitorus
Krisnia Siahaan
Bryan Simamora
Nikita Simanjuntak

Journal of Intelligent Computing, Systems, and Applications (JICSA)
Vol. 1 No. 02 (2026): March 2026

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.