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An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics
یک رویکرد هوشمند برای طراحی متا تحقیق تجارت الکترونیک و سیستم رتبه بندی با استفاده از تجزیه و تحلیل داده های بزرگ نسل آینده-2018 The purpose of this research work is to explore various limitations of conventional search and page rank
ing systems in an E-Commerce environment. The key objective is to assist customers in making an online
purchase decision by providing personalized page ranking order of E-Commerce web links in response to
E-Commerce query by analyzing the customer preferences and browsing behavior. This research work
first employs an orderly and category wise literature review. The findings reveal that conventional search
systems have not evolved to support big data analysis as required by modern E-Commerce environment.
This work aims to develop and implement second-generation HDFS- MapReduce based innovative page
ranking algorithm, i.e. Relevancy Vector (RV) algorithm. This research equips the customer with a robust
metasearch tool, i.e. IMSS-AE to easily understand personalized search requirements and purchase pref
erences of customer. The proposed approach can well satisfy all critical parameters such as scalability,
partial failure support, extensibility as expected from next-generation big data processing systems. An
extensive and comprehensive experimental evaluation shows the efficiency and effectiveness of pro
posed RV page ranking algorithm and IMSS-AE tool over and above other popular search engines.
Keywords: E-Commerce website ranking ، IMSS- AE tool ، RV page ranking algorithm ، Second generation big data analytics ، Hadoop-MapReduce ، Personalized page ranking |
مقاله انگلیسی |