The Comparative Analysis of Item-Based Collaborative Filtering and Alternating Least Squares on Implicit Feedback Recommender Systems
DOI:
https://doi.org/10.54074/jicsa.v1i02.21Keywords:
Recommender System, Implicit Feedback, Collaborative Filtering, Alternating Least Squares, MAP@10Abstract
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.
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