Scalable Matrix Factorization and ALS-Based Recommendation Systems on MovieLens 32M

This project explores scalable recommendation systems based on matrix factorization, with a focus on Alternating Least Squares (ALS) for large-scale collaborative filtering.

The work progresses from baseline matrix factorization models to content-aware and hybrid recommender systems, addressing key challenges such as data sparsity, scalability, and efficient optimization in large user–item interaction matrices. Experiments are conducted on the MovieLens 32M dataset, enabling rigorous evaluation of convergence behavior, recommendation quality, and computational performance.

Ongoing work focuses on extending the ALS framework with auxiliary information to improve recommendation accuracy while maintaining scalability, with the goal of preparing a journal or arXiv manuscript.

• Project report / materials:
  Google Drive – Project Documentation