دانلود رایگان مقاله انگلیسی رویکردی هوشمند برای طراحی ابرجستجوی تجارت الکترونیکی و رتبه بندی سیستم - الزویر 2018

abstract

The purpose of this research work is to explore various limitations of conventional search and page ranking 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 preferences 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 proposed RV page ranking algorithm and IMSS-AE tool over and above other popular search engines.

Conclusion and future work

This research paper presents a Hadoop- Map Reduce based personalized E-Commerce search framework for the second generation big data analytics. The research gap is shown in this study by submitting various conventional search systems in the form of detailed category wise literature review. This research work proposes a novel RV page ranking algorithm and implements the same as an E-Commerce website ranking tool, i.e., Intelligent Meta Search System for Advanced E-Commerce. The IMSS- AE tool can assist modern day customer in choosing appropriate ECommerce website for online purchase of a product. The efficiency of proposed ranking approach is justified by experimental analysis. The graphical evaluation for comparison of personalized precision of IMSS-AE tool over Yahoo, Dogpile, Google, and IMSS- SE tool demonstrates the effectiveness of proposed approach over conventional & professional page ranking methods. The practical implications for three different audiences of this research work are as follows: Practical Implication for End User- The end user of this research work is an online customer willing to make an online transaction. The result of this research work in the form of IMSS-AE tool can assist the customers in the suitable ranking of E-Commerce websites for the purchase of a specific product. The end user will be benefitted by personalized website ranking output and hence can easily select a website that is most appropriate for satisfying the online purchase needs of a user.