Published in: 2nd International Conference on Electrical Engineering
COI code: ICELE02_456
Paper Language: English
How to Download This Paper
For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.
Authors Recommendation Systems based on Association Rule Mining for a Target Object by Evolutionary AlgorithmsHossein Hatami Varzaneh - Department of Computer Engineering, Faculty of Computer & Electrical Engineering, Kashan Branch, Islamic Azad University,Kashan, Isfahan, Iran
Behzad Soleimani Neysiani - Department of Computer Engineering, Faculty of Computer & Electrical Engineering, University of Kashan, Kashan, Isfahan, Iran
Hassan Ziafat - Young Researcher and Elite Club, Natanz Branch, Islamic Azad University, Natanz, Isfahan, Iran
Abstract:Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done to use associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and can not be used in real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality
Keywords:Recommander Systems; Collaborative Filtering; Association Rule Mining; Multi-Objective Evolutionary Algorithms; Particle Swarm Optimization; Genetic
COI code: ICELE02_456
how to cite to this paper:If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Hatami Varzaneh, Hossein; Behzad Soleimani Neysiani & Hassan Ziafat, 2017, Recommendation Systems based on Association Rule Mining for a Target Object by Evolutionary Algorithms, 2nd International Conference on Electrical Engineering, تهران, دانشگاه علامه مجلسي, https://www.civilica.com/Paper-ICELE02-ICELE02_456.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Hatami Varzaneh, Hossein; Behzad Soleimani Neysiani & Hassan Ziafat, 2017)
Second and more: (Hatami Varzaneh; Soleimani Neysiani & Ziafat, 2017)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)
The University/Research Center Information:
Type: Azad University
Paper No.: 1374
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.
Research Info Management
Export Citation info of this paper to research management softwares
New Related Papers
- Applying local optimization algorithms in clustering combination with diversity maximization
- Gain-Bandwidth Enhancement in Folded-Cascode Op-Amp
- A new technique for obtaining step response of the two degrees of freedom gyro stabilized platform
- Calibrated Audio Steganalysis
- Key management system for WSNs based on hash functions and elliptic curve cryptography
The Above articles are recently indexed in the related subjects
Iran Scientific Advertisment Netword
Share this paper
WHAT IS COI?
COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.