Abstract
The offline evaluation of recommender systems is typically based on accuracy metrics such as the Mean Absolute Error and the Root Mean Squared Error for error rating prediction and Precision and Recall for measuring the quality of the top-N recommendations. However, it is difficult to reproduce the results since there are various 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 results might vary. In this paper, we show that within 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 wide range of recommendation algorithms ranging from collaborative filtering to complicated fuzzy recommendation approaches that can solve the filter bubble problem, a real dataset, and the results show that it is both practical and effective.
Highlights
Recommender systems are widely known for their use in e-Commerce for recommending products to users, reducing the overall searching time of the user and increase sales
For the offline evaluation of recommender systems various metrics can be used such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for predicting the accuracy error and information retrieval metrics such as Precision and Recall can be used for measuring the quality of the top-N recommendations [4]
In this paper we have proposed an approach that is based on explanations
Summary
Recommender systems are widely known for their use in e-Commerce for recommending products to users, reducing the overall searching time of the user and increase sales. It is a technology used in various other less known domains such as music recommendation or people to people recommendation in social media [1,2]. The increasing use and popularity of recommender systems research both in academia and in industry has led to the development of new algorithms and their experimental evaluation. In the literature there are various libraries that can be used for developing and testing a recommendation algorithm and include
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