An intelligent approach to design of E-Commerce metasearch and ranking system using next-generation big data analytics
| Autor: | O. P. Rishi, Dheeraj Malhotra |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
General Computer Science business.industry Computer science Big data 020206 networking & telecommunications 02 engineering and technology E-commerce IMSS- AE tool lcsh:QA75.5-76.95 Second generation big data analytics Personalized search RV page ranking algorithm Ranking E-Commerce website ranking Order (business) Scalability Personalized page ranking 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science business Metasearch engine Hadoop-MapReduce |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 33, Iss 2, Pp 183-194 (2021) |
| ISSN: | 1319-1578 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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