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.
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.
guide/introduction guide/install guide/quick_start Release Notes <https://github.com/sisinflab/elliot/releases>
guide/config guide/data_prep
guide/alg_intro guide/hyper_optimization guide/new_alg guide/recommenders
guide/metrics_intro guide/metrics_summary
elliot/elliot elliot/elliot.dataset elliot/elliot.evaluation elliot/elliot.namespace elliot/elliot.hyperoptimization elliot/elliot.prefiltering elliot/elliot.recommender elliot/elliot.result_handler elliot/elliot.splitter elliot/elliot.utils
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