Examination of Early Feedbacks for Effective Product Retailing on E-Commerce Websites
R. Velvizhi1, S. Sri Gowtham2, D. Jeya Priya3
1R.Velvizhi, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2S. Sri Gowtham, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3D. Jeya Priya, Department of CSE, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 14 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 10 October 2019 | PP: 703-706 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F12590886S219/19©BEIESP | DOI: 10.35940/ijeat.F1259.0886S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Online reviews became a essential information base for customers prior to the creation of the buy call affiliate. Early item reviews tend to have a strong effect on the following item revenues. Throughout this article, we tend to take the action to check early reviewers ‘ behavioral features through their announcement videos on two real-world gigantic e-commerce platforms, i.e., Amazon. Specifically, we tend to split the item cycle into three successive phases, especially early, majority and laggards. A person who published a review early on is considered as an early review associate We tend to quantitatively characterize early critics who have endorsed their ranking behaviors, the helpfulness results obtained from others, and hence the correlation between their ratings and the performance of the item. We discovered that combine early reviewers tend to give a stronger median rating score; linked with[ 2] early reviewers tend to publish more helpful feedback. Additionally, our item reviews assessment shows the ratings of these early reviewers and their earned helpfulness scores square measure that can affect the performance of the item. By watching the posting technique of evaluation as a competitive multiplayer game, we tend to suggest a totally distinctive embedding model for early reviewer prediction. Intensive tests on 2 completely distinct e-commerce datasets have shown that our suggested strategy exceeds various competitive baselines.
Keywords: Product Retailing, E-Commerce, Electronic Buisness.
Scope of the Article: Software Product Lines