The book every data scientist needs on their desk.

⚡️A Blazing-Fast Python Library for Ranking Evaluation, Comparison, and Fusion 🐍

Movie recommendation system with Python. Implements content-based filtering (TF-IDF + cosine similarity), collaborative filtering with matrix factorization (TruncatedSVD), and a hybrid approach. Evaluates with Precision@K, Recall@K, and NDCG. Includes rating distribution plots, top movies, and sample recommendations.

Sorbetto: a Python Library for Producing Classification Tiles With Different Flavors

A Framework for validating two ranked lists using ndcg, kendall's tau, rbo, recall, mrr

🚀 Production-ready hybrid recommender system for e-commerce. Combines collaborative filtering & content-based ML with FastAPI backend, React

A hands-on recommendation project exploring baseline ranking methods and an ARL-inspired policy for improving item suggestions. The notebook walks through data preparation, modeling, evaluation, and insights, offering a clear and reproducible workflow for experimenting with recommender systems.

A repository to understand ranking metrics as described by Musgrave et al. (2020). Plus some other metrics utilising confidence values.

Code for "The optimal size of ranking lists"

Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics Volume 4: Student Research Workshop)

Recommendation Evaluation Metrics Package

Information Retrieval Metrics Simulation