Advanced Recommender System

Fri, 20 Jun 2025 00:00:00 +0000

This project was developed within the CESMA Master’s program in collaboration with TIM. Instead of framing the problem as a standard classification task, the system was designed as a learning-to-rank pipeline for next-best-action recommendation.

That shift in framing matters because ranking is closer to the actual business decision: not just whether an action is good or bad, but which action should come first for a given user.

The pipeline combines careful validation, Bayesian optimization, and ensemble ranking strategies. The end result is a substantial improvement over baseline performance on NDCG@5, making the project a solid example of applied machine learning under realistic evaluation constraints.

Performance summary:

Stage	NDCG@5 Score	Improvement vs Baseline
Baseline Model	0.5030	–
Best Single Model	0.6838	+35.94%
Best Ensemble	0.6852	+36.23%

Overall, it is one of the clearest examples in the portfolio of taking a familiar ML task and reformulating it in a way that is better aligned with the actual decision problem.

Recommender Systems | Stefano Blando

Advanced Recommender System