Factual Product Recommendation System by Eliminating Fake Reviews using Machine Learning Techniques - K.R.Prasanna Kumar, M.Pranesh, T.G.Rakhul Raahje, P.Vignesh, Dr.K.Kousalya, K.Logeswaran (International Journal of Advanced Science and Technology, 2020)
The E-Commerce platform is rapidly growing at an unprecedented rate around the world which attractspeople from all age groups to satisfy their shopping needs. It gives more satisfaction to their users ascompared to retail shopping due to few reasons like huge number of e-commerce stores, easy to find aproduct on e-store, provides detailed information of the product and e-stores provides customers reviewsabout the product. Recent research states that the user feedback plays a vital role in deciding the sales ofthe product. The customers reviews are huge in volume, which consists of actual feedback from customersand fake feedback from spammers. So, the extraction of genuine review information is a difficult task forthe customers, manufactures and others who interested in customer feedback. In this paper Factual ProductRecommendation System (FPRS) is introduced. FPRS is an effective technique to solve fake reviewsproblem and provide genuine reviews. FPRS incorporates the Naive Bayes Classifier (NBC) and LatentDirichlet Allocation (LDA) to sanitize and extract the genuine reviews and also includes the MatrixFactorization (MF) to provide products recommendations.