Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/660
Title: Multi-criteria collaborative filtering using rough sets theory
Authors: Demirkıran, Emin Talip
Pak, Muhammet Y.
Çekik, Rasim
Keywords: Accuracy
multi-criteria collaborative filtering
recommender systems
rough sets theory
Issue Date: 2021
Publisher: Ios Press
Abstract: Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance.
URI: https://doi.org/10.3233/JIFS-201073
https://hdl.handle.net/20.500.13087/660
ISSN: 1064-1246
1875-8967
Appears in Collections:Kimya Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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