sisinflab/elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
For researchers working on recommender systems, this project helps evaluate and compare different recommendation algorithms. You provide your dataset and a configuration file outlining the experimental setup, and it produces a detailed report on model performance, including various metrics, statistical analyses, and optimized hyperparameters. It's designed for academics and data scientists focused on rigorous evaluation of recommendation models.
296 stars.
Use this if you need a comprehensive, reproducible framework to evaluate the effectiveness of various recommender system algorithms on your datasets, comparing them robustly.
Not ideal if you're looking for a simple tool to deploy an off-the-shelf recommender system without needing in-depth comparative evaluation or hyperparameter optimization.
Stars
296
Forks
56
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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