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.

Introduction

In this modern era of big data, the shopping activity is modified a lot because of enormous growth in online shopping websites, also known as E-Tailers. The new age customers prefer to shop through these online portals because of various attractions in countries like India such as easy and cheap availability of the Internet. The primary reason is intense competition between telecoms, for instance, Reliance Jio prime membership offers free unlimited internet data usage for three months for all of its users in nominal charges. Some of the other reasons include lucrative cash back and easy returns without deduction of shipping charges from portals like PayTm, Cash on delivery type regular features from E-Commerce sites like Flipkart, Amazon, and other E-Tailers. Moreover, searching a suitable E-Commerce website to best suit the customer purchase requirements is not so easy as customers are primarily dependent on conventional search engines like Google, Bing to find a suitable E-Commerce web site. However, when different users search the same E-Commerce query, even a most advanced and popular search engine retrieves the same result as discussed by Gomez-Nieto et al. (2014). Thus, irrespective of the background and personalized tastes of customer submitting the query as most of the modern search engines tend to return the results by interpreting the E-Commerce query in various possible ways. Moreover, if the query is ambiguous or incomplete, then the situation will get even worse as discussed by Malhotra and Verma (2013). For instance, for the incomplete E-Commerce search query ‘‘Galaxy”, some customers may be interested in links to buy a new Samsung Galaxy series mobile phone, while another customer may be interested in searching links for online booking of tickets for a movie Guardians of the Galaxy Vol. 2. Hence, there is an urgent need for personalized E-Commerce search system. The personalized system may modify the E-Commerce search query by keeping track of customer’s preferences by maintaining his/her profile, search preferences through browsing history, etc. over a period and return results in correct order of ranking with customer’s relevant output links on top to best suit the customer requirements (See Fig. 1).