Skip to Main content Skip to Navigation
Conference papers

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

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download

https://hal.inria.fr/hal-02331301
Contributor : Hal Ifip <>
Submitted on : Thursday, October 24, 2019 - 12:50:11 PM
Last modification on : Thursday, October 24, 2019 - 12:54:44 PM
Long-term archiving on: : Saturday, January 25, 2020 - 3:27:57 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Antonios Papaleonidas, Elias Pimenidis, Lazaros Iliadis. Hybrid Data Set Optimization in Recommender Systems Using Fuzzy T-Norms. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.647-659, ⟨10.1007/978-3-030-19823-7_54⟩. ⟨hal-02331301⟩

Share

Metrics

Record views

59