Welcome to Elliot’s documentation!

Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher’s perspective. It conducts a whole experiment, from dataset loading to results gathering. The core idea is to feed the system with a simple and straightforward configuration file that drives the framework through the experimental setting choices. Elliot untangles the complexity of combining splitting strategies, hyperparameter model optimization, model training, and the generation of reports of the experimental results.

system schema

system schema

The framework loads, filters, and splits the data considering a vast set of strategies (splitting methods and filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot optimizes hyperparameters for several recommendation algorithms, selects the best models, compares them with the baselines providing intra-model statistics, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test).

Elliot aims to keep the entire experiment reproducible and put the user in control of the framework.



Indices and tables