Title: Decision-making strategy for detecting authenticated recommendations and identification of valuable customers in online shopping sites

Authors: Goldina Ghosh; Bidushi Chakraborty

Addresses: Department of BCA, Institute of Engineering and Management, Kolkata, West Bengal-700091, India ' Department of Mathematics, Institute of Engineering and Management, Kolkata, West Bengal-700091, India

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.

Keywords: chain of connectivity; buying frequency; valuable customers; scattered graph; decision tree learning; cross matrix.

DOI: 10.1504/IJBDA.2020.112202

International Journal of Business and Data Analytics, 2020 Vol.1 No.4, pp.351 - 370

Received: 23 Sep 2019
Accepted: 28 Mar 2020
Published online: 04 Jan 2021 *

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