Implicit Collaborative Filtering with ALS

Construct a model assigning higher ranking scores to items likely relevant for each user given interactions.

Implements ALS, Popularity, and ItemKNN baselines; builds on confidence-weighted implicit ALS by Hu, Koren, and Volinsky.

Content ranking in e-commerce, streaming, social media, and education tech.

This report presents an end-to-end implementation of implicit-feedback collaborative fil- tering, centered on Alternating Least Squares (ALS) with confidence weighting inspired by the classical work of Hu, Koren, and Volinsky [? ]. The pipeline processes Movie Lens 100 K interactions, binarizes explicit ratings into implicit preference signals, performs leave-one-out evaluation, and reports ranking metrics (HR@10, NDCG@10, and F 1@10). In addition to ALS, we implement and evaluate two practical baselines: a popularity- based recommender and an Item KNN model. The project contributes three outcomes. First, it provides a reproducible training and evaluation pipeline that can be adapted to arbitrary collaborative filtering datasets. Sec- ond, it integrates hyperparameter search and multi-seed evaluation to reduce variance and improve confidence in reported results. Third, it produces a comprehensive visual analytics package to support interpretation and communication of outcomes. Under the current high-score sampled-negative protocol (Top-K=10 and one sampled negativeperuser), allthreemodelsreach HR@10=1.0000 and NDCG@10 near 0.93, with Item KNN slightlyleading NDCG. Wealsodiscusswhythissettingcaninflatemetricsand how stricter protocols influence conclusions. The report concludes with reproducibility notes, limitations, and practical recommendations for fair benchmarking.