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Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms

Papaleonidas, Antonios; Pimenidis, Elias; Iliadis, Lazaros


Antonios Papaleonidas

Lazaros Iliadis


John MacIntyre

Ilias Maglogiannis

Lazaros Iliadis


© 2019, IFIP International Federation for Information Processing. A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually 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. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.


Papaleonidas, A., Pimenidis, E., & Iliadis, L. (2019). Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms. In J. MacIntyre, I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), .

Online Publication Date May 12, 2019
Publication Date Jan 1, 2019
Deposit Date May 29, 2019
Publisher Springer Verlag (Germany)
Volume 559
Pages 647-659
Series Title IFIP Advances in Information and Communication Technology
Series Number 559
Keywords recommender systems, evaluation, explanations,reproducibility, fuzzy logic, t-norms
Public URL
Publisher URL
Additional Information Additional Information : The final publication is available at Springer via


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