Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation (2103.02590v2)

Abstract: Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. Puzzled and frustrated by the continuous recreation of appropriate evaluation benchmarks, experimental pipelines, hyperparameter optimization, and evaluation procedures, we have developed an exhaustive framework to address such needs. Elliot is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple configuration file. The framework loads, filters, and splits the data considering a vast set of strategies (13 splitting methods and 8 filtering approaches, from temporal training-test splitting to nested K-folds Cross-Validation). Elliot optimizes hyperparameters (51 strategies) for several recommendation algorithms (50), selects the best models, compares them with the baselines providing intra-model statistics, computes metrics (36) spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis (Wilcoxon and Paired t-test). The aim is to provide the researchers with a tool to ease (and make them reproducible) all the experimental evaluation phases, from data reading to results collection. Elliot is available on GitHub (https://github.com/sisinflab/elliot).

• The paper introduces Elliot (R2E2), a framework for reproducible evaluation of recommender systems by automating experimental pipelines.
• It implements 13 data splitting methods, 8 prefiltering strategies, 50 algorithms, and 51 hyperparameter optimization techniques to ensure rigorous testing.
• The framework enhances reliability by addressing evaluation biases and standardizing protocols, thereby streamlining recommender system research.

Essay: $R2E2$: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

The presented research introduces $R2E2$, a framework designed to address the challenges of reproducible and comprehensive evaluation within Recommender Systems (RSs). The proliferation of recommendation algorithms and various evaluation methodologies has rendered rigorous assessments difficult. Authors Anelli et al. have crafted $R2E2$ to ameliorate these intricacies by automating experimental pipelines through a configurable framework.

Key components of $R2E2$ allow for extensive data processing, algorithm execution, and performance evaluation. The framework supports 13 data splitting methods and 8 prefiltering strategies, which enhance reproducibility across different evaluation setups. The inclusion of diverse splitting methods such as hold-out, cross-validation, and temporal splits ensures versatility across varied recommendation tasks. Additionally, $R2E2$ supports 50 recommendation algorithms, a notable breadth that encapsulates classical, latent factor, deep learning, and graph-based methods, among others.

The hyperparameter optimization within $R2E2$ leverages 51 strategies, including grid search, random search, and Bayesian optimization, integrating advanced techniques from the HyperOpt library. This extensive support for tuning ensures that the parameter space is explored efficiently, contributing to the generation of competitive baselines. Evaluative metrics span from accuracy to fairness and bias, totalling 36 in number, accompanied by statistical tests like the Wilcoxon and Paired t-test to validate the significance of results.

The framework's dedication to addressing reproducibility challenges is underscored by its careful consideration of data preprocessing and evaluation biases. The authors explicitly address known issues such as suboptimal baseline testing and unreproducible results by providing a systematized experimental workflow.

From a practical standpoint, $R2E2$ has the potential to streamline research processes significantly, enabling researchers to focus primarily on hypothesis testing rather than the cumbersome intricacies of data handling and model evaluation. The implications of such a framework include establishing fidelity in reported results and potentially nurturing a culture of reliability within the RS community. The documented effectiveness of latent-factor models when comprehensively tuned against deep learning methods points to significant findings that require further exploration.

Theoretically, the introduction of $R2E2$ promotes more rigorous experimentation protocols, possibly encouraging the community toward a consensus on evaluation standards. Furthermore, the modular and extendable nature of the framework means it can adapt to future developments in RSs, such as incorporating sequential learning paradigms or privacy-preserving recommendations.

In conclusion, $R2E2$ is a critical contribution to advancing the depth of evaluation protocols within RSs, proposing a robust solution to prevalent reproducibility issues. Future work might explore integrating reinforcement learning strategies and adversarial learning to maintain the framework's state-of-the-art status. As the field progresses, $R2E2$ may well become an indispensable tool for both the development and validation of recommender systems across various application domains.