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JCR Impact Factor: 0.699
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,406 KB) | Citation | Downloads: 189 | Views: 259

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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Full text preview
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: 20,438 TCR
SCOPUS® Citations for all references: 8,514 TCR

Web of Science® Average Citations per reference: 465 ACR
SCOPUS® Average Citations per reference: 194 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 2019-05-20 02:40 in 205 seconds.




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