Decision-making strategy for detecting authenticated recommendations and identification of valuable customers in online shopping sites. (23rd December 2020)

• Record Type: Journal Article
• Authors: Ghosh, Goldina; Chakraborty, Bidushi
• Is Part Of: International journal of business and data analytics. Volume 1:Number 4(2019)
• Page Start: 351
• Page End: 370
• Publication Date: 2020-12-23
• Subjects: chain of connectivity -- buying frequency -- valuable customers -- scattered graph -- decision tree learning -- cross matrix; Commercial statistics -- Data processing -- Periodicals; Industrial management -- Mathematical models -- Periodicals; Business -- Mathematical models -- Periodicals; Management -- Statistical methods -- Periodicals; Business -- Research -- Periodicals; 658.403
• Languages: English
• ISSNs: 2515-9100

Abstract

The online shopping sites help customers not only choose various products but also place their reviews on those products. Their choices and opinions are often beneficial to other customers who want to buy the product. In this paper, we propose a method to identify only the best recommenders and the best buyers to validate the authenticity of the opinions and they are denoted as the valuable customers. The number of customers being influenced by the recommender develops a link or connection, this leads to a chain of connectivity. This concept has been justified by applying graph theory notion. Since the online sites are dynamic in nature, hence to keep the record set updated with valuable customers' information decision tree learning technique has been implemented. The effectiveness of the method is tested by applying it to a data set obtained from Amazon.