  In the rapidly evolving domain of Recommender Systems (RecSys), new
algorithms frequently claim state-of-the-art performance based on evaluations
over a limited set of arbitrarily selected datasets. However, this approach may
fail to holistically reflect their effectiveness due to the significant impact
of dataset characteristics on algorithm performance. Addressing this
deficiency, this paper introduces a novel benchmarking methodology to
facilitate a fair and robust comparison of RecSys algorithms, thereby advancing
evaluation practices. By utilizing a diverse set of $30$ open datasets,
including two introduced in this work, and evaluating $11$ collaborative
filtering algorithms across $9$ metrics, we critically examine the influence of
dataset characteristics on algorithm performance. We further investigate the
feasibility of aggregating outcomes from multiple datasets into a unified
ranking. Through rigorous experimental analysis, we validate the reliability of
our methodology under the variability of datasets, offering a benchmarking
strategy that balances quality and computational demands. This methodology
enables a fair yet effective means of evaluating RecSys algorithms, providing
valuable guidance for future research endeavors.
