Are the Customers Receiving Exact Recommendations from the E-Commerce Companies? Towards the Identification of Gray Sheep Users Using Personality Parameters

doi:10.23940/ijpe.23.07.p1.425433

Abstract

Abstract: In the real-world, use of digital media is increasing day by day. Consumers are always in a dilemma when they have a lot of choices and they always get confused among them. The E-commerce companies want to increase their profit, so they introduced recommender systems. The recommender system is like the newspaper which gives us recommendations when we do not have any information about the particular products of the market. Users’ mind diverts the fetching of data from users’ favorite headlines just like the recommender system which fetches the items according to the users’ taste and preferences. However, to date Recommender systems have constraints of cold start problems, gray sheep user problems and shilling attacks. Variety in the tastes of the users gives rise to the gray sheep problem where it is difficult to match the products as per the choice of one person’s taste to the other person. This affects the performance of recommender systems. In this paper, the chocolate bars flavors_of_Cacao dataset (124KB) with 1796 entries tuples and 9 attributes dataset based on the personality parameters (modeling with Big Five model), is analyzed with boosted decision trees, two neural networks, logistic regression and decision forest. The accuracy of the decision forest is validated over other machine learning algorithms for a recommendation of the chocolate bars.

Key words: machine learning, Gray Sheep users, recommender system, personality models, Chocolate Bars popularity

Babaljeet Kaur and Shalli Rani. Are the Customers Receiving Exact Recommendations from the E-Commerce Companies? Towards the Identification of Gray Sheep Users Using Personality Parameters [J]. Int J Performability Eng, 2023, 19(7): 425-433.

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