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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2018 - 3

Cascaded Feature Selection for Enhancing the Performance of Collaborative Recommender System

AL-SHAMRI, M. Y. H. See more information about AL-SHAMRI, M. Y. H. on SCOPUS See more information about AL-SHAMRI, M. Y. H. on IEEExplore See more information about AL-SHAMRI, M. Y. H. on Web of Science, AL-JUNIAD, A. F. See more information about  AL-JUNIAD, A. F. on SCOPUS See more information about  AL-JUNIAD, A. F. on SCOPUS See more information about AL-JUNIAD, A. F. on Web of Science, QAID, T. S. See more information about  QAID, T. S. on SCOPUS See more information about  QAID, T. S. on SCOPUS See more information about QAID, T. S. on Web of Science, AHMED, M. H. A. See more information about  AHMED, M. H. A. on SCOPUS See more information about  AHMED, M. H. A. on SCOPUS See more information about AHMED, M. H. A. on Web of Science, RAWEH, A. A. See more information about RAWEH, A. A. on SCOPUS See more information about RAWEH, A. A. on SCOPUS See more information about RAWEH, A. A. on Web of Science
 
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Author keywords
computational modeling, feature extraction, information filtering, machine learning, recommender systems

References keywords
systems(18), recommender(16), learning(9), machine(8), data(8), collaborative(7), system(6), selection(6), recommendation(6), filtering(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 23 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04003
Web of Science Accession Number: 000451843400003
SCOPUS ID: 85058813839

Abstract
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Most of collaborative recommender systems (CRSs) rely on statistical and data analysis methods for comparing users. However, dealing with them using machine learning techniques seems to be more appropriate. This paper investigates the usage of feature selection and classification methods for CRSs. It suggests building a user model suitable for the classification purpose and proposes a density-based feature selection (DBFS) method based on the rating density for each class. The DBFS reduces the effect of sparsity problem and keeps only users having a dense-feature history. Additionally, a cascaded feature selection method is proposed to pick out a subset of features through a two-layer approach. The first layer applies a classical feature selection method while the second layer applied the DBFS on the output of the first layer. The results show that the performance is gradually improved. The cascaded feature selection yields the best results since it improves the system accuracy, reduces the space and processing complexities, and alleviates the sparsity in two cascaded layers. The achieved improvements by cascaded feature selection as compared to SVM are 6.55 percent, 10.14 percent, and 3.92 percent in terms of accuracy, F-measure and MAE, respectively.


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References Weight

Web of Science® Citations for all references: 0
SCOPUS® Citations for all references: 25,657 TCR

Web of Science® Average Citations per reference: 0
SCOPUS® Average Citations per reference: 583 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2025-07-01 00:27 in 192 seconds.




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