RECOMMENDATION ENGINE: PERSONALIZED APPROACH
Journal of Critical Reviews · 2020
With Personalized and Efficient performance taken as primary goals, this paper focuses on building a recommendation engine. We have proposed a method which recommends product based on the user's like and dislike. It focuses on building a Restricted Boltzmann Machine which suggests a product to buyers and helps in providing ease while shopping. The reader gets an insight into how RBM helps in having a good recommending engine as compared to other long-running traditional methods. In this paper, we have explored the use of RBM with two layers by converting tabular data into a user-item matrix.