An explanation-based approach for experiment reproducibility in recommender systems

Polatidis, Nikolaos; Papaleonidas, Antonios; Pimenidis, Elias; Iliadis, Lazaros

Abstract

© 2019, Springer-Verlag London Ltd., part of Springer Nature. 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.

• Journal Article Type: Article
• Acceptance Date: May 21, 2019
• Online Publication Date: May 27, 2019
• Deposit Date: May 29, 2019
• Publicly Available Date: May 28, 2020
• Journal: Neural Computing and Applications
• Print ISSN: 0941-0643
• Electronic ISSN: 1433-3058
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s00521-019-04274-x
• Keywords: recommender systems, explanations, reproducibility, fuzzy logic
• Public URL: https://uwe-repository.worktribe.com/output/846598
• Publisher URL: https://doi.org/10.1007/s00521-019-04274-x
• Additional Information: Additional Information : This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s00521-019-04274-x.
• Contract Date: May 29, 2019