A Systematic Learning on Variety of Recommender Systems for Online Commodities
D. Anand Joseph Daniel1, M. Janaki Meena2
1D. Anand Joseph Daniel, Assistant Professor, Department of Computer Science and Engineering, Anand Institute of Higher Technology, Chennai, Tamil Nadu, India.
2Dr. M. Janaki Meena, Professor, School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 08 July 2019 | Manuscript published on 30 August 2019 | PP: 1244-1252 | Volume-8 Issue-10, August 2019 | Retrieval Number: H6969068819/2019©BEIESP | DOI: 10.35940/ijitee.H6969.0881019
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© 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: In a sophisticated high-end product market, all firms often come up with a vast number of goods to partake the market shares. Owing to the availability of enough information of various products that enters the market or due to lack of right information, customers are prone to the state of dilemma in comparing and choosing the most appropriate ones. In most of the cases, the product specifications are mentioned, still whether these features suit the customers need is a concern. Online reviews tend to benefit the consumers and the goods developers. Here again, finding out the more supportive reviews become a challenge. Considering these factors, this article intends to be particular in reviewing the existing evaluation strategies and recommender systems that have grown progressively favorable in present era and are employed widely for casual to commercial items.
Keywords: High-end products, Online reviews, Customer needs, Evaluation strategies, Recommender system.
Scope of the Article: Marketing and Social Sciences