CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
Reseach Article

E-Commerce Grading Approaches of Ultimate Ranking System- A Review

by Gurkiran Kaur, Rekha Bhatia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 144 - Number 8
Year of Publication: 2016
Authors: Gurkiran Kaur, Rekha Bhatia
10.5120/ijca2016910423

Gurkiran Kaur, Rekha Bhatia . E-Commerce Grading Approaches of Ultimate Ranking System- A Review. International Journal of Computer Applications. 144, 8 ( Jun 2016), 33-36. DOI=10.5120/ijca2016910423

@article{ 10.5120/ijca2016910423,
author = { Gurkiran Kaur, Rekha Bhatia },
title = { E-Commerce Grading Approaches of Ultimate Ranking System- A Review },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 144 },
number = { 8 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume144/number8/25203-2016910423/ },
doi = { 10.5120/ijca2016910423 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:09.396692+05:30
%A Gurkiran Kaur
%A Rekha Bhatia
%T E-Commerce Grading Approaches of Ultimate Ranking System- A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 144
%N 8
%P 33-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The e-commerce ranking algorithms are the algorithms utilized for the purpose of product suggestions and product listings against the input query or the browsed product on the given shopping portal. The product ranking portals requires the number of computations on the basis of various factors for the calculation of the ranks of the given products. Various local and online factors can be grouped for the overall evaluation of the product ranks according to the input keywords specifically on the first stage product lookup on the shopping sites. In this paper, the product ranking solution has been proposed with the versatile approach using the popularity and accessibility factors. Also the reliability factors are evaluated which analyzes the trust factor for the page by using the online security evaluation programs. Various experiments would be conducted over the large number of input product data obtained from the application programming interfaces (API) from the prominent shopping portals active online. The proposed model is expected to resolve the issue by evaluating the proposed model performance in comparison with the existing model on the basis of various factor associated with the time complexity and reliability.

References
  1. Verma, Neha, Dheeraj Malhotra, Monica Malhotra, and Jatinder Singh. "E-commerce Website Ranking Using Semantic Web Mining and Neural Computing." Procedia Computer Science 45 (2015): pp. 42-51, ELSEVIER.
  2. Hepp, Martin. "The Web of Data for E-Commerce: Schema. org and GoodRelations for Researchers and Practitioners." In Engineering the Web in the Big Data Era, pp. 723-727. Springer International Publishing, 2015.
  3. Sessoms, Matthew, and Kemafor Anyanwu. "Enabling a Package Query Paradigm on the Semantic Web: Model and Algorithms." In Transactions on Large-Scale Data-and Knowledge-Centered Systems XIII, pp. 1-32. Springer Berlin Heidelberg, 2014.
  4. Malhotra, Dhairya. "Intelligent web mining to ameliorate Web Page Rank using Back-Propagation neural network." In Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference-, pp. 77-81. IEEE, 2014.
  5. Mital, Monika, Ashis Pani, and Ram Ramesh. "Determinants of choice of semantic web based Software as a Service: An integrative framework in the context of e-procurement and ERP." Computers in Industry 65, no. 5 (2014): 821-827
  6. Furukawa, Takao, Kaoru Mori, Kazuma Arino, Kazuhiro Hayashi, and Nobuyuki Shirakawa. "Identifying the evolutionary process of emerging technologies: A chronological network analysis of World Wide Web conference sessions." Technological Forecasting and Social Change 91 (2015): 280-294.
  7. Scioscia, Floriano, Michele Ruta, Giuseppe Loseto, Filippo Gramegna, Saverio Ieva, Agnese Pinto, and Eugenio Di Sciascio. "A Mobile Matchmaker for the Ubiquitous Semantic Web." International Journal on Semantic Web and Information Systems (IJSWIS) 10, no. 4 (2014): 77-100.
  8. D.T. Green and J. M. Pearson, “The examination of two web site usability instruments for use in B2C e-commerce organizations,” Journal of Computer Information Systems, Vol. 49, No. 4, 2009, pp. 19-32
  9. T. Wang and Y. Lin, “Accurately predicting the success of B2B ecommerce in small and medium enterprises,” Expert Systems with Applications, Vol. 36, No. 2, published by Elsevier, 2009, pp. 2750–2758.
  10. M. Lazarica and I. Lungu, Aspecte privind proiectarea sistemelor de comert electronic, ASE Publishing House, 2007, pp. 147.
Index Terms

Computer Science
Information Sciences

Keywords

Ranking system semantic ranking machine learning e-commerce ranking semantic web neural network.