Reproduction of Experiments in Recommender Systems Evaluation Based on Explanations

Abstract

The offline evaluation of recommender systems is typically based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. However, it is difficult to reproduce the results since there are different libraries that can be used for running experiments and also within the same library there are many different settings that if not taken into consideration when replicating the result might vary. In this paper, we show that it is challenging to reproduce results using a different library but with the use of the same library an explanation based approach can be used to assist in the reproducibility of experiments. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is both practical and effective.