Published Paper Details:

The recommendation system is made for suggesting best products for users as per their conveniences. In this system Individual suggestions are Important for making in general future decisions this data will be useful in upcoming ages. DRL has been a good option for E-com. This system will focus on multiple hops rather than single hops. This could lead to optimal suggestions that's why Graph based neural network has been used in this system. Also, this system propagates use of LSTM. Here in this system, we are using Interactive recommendations by using LSTM which is useful for predicting long term dependencies in the system. This will lead to more interactive and speedy suggestions. Social graph neural network and LSTM is going to play huge role in this recommendation system which will suggest proper products through E-com system. LSTM will be useful for speeding the processing rate which is a part of RNN and will help system to understand past events and user preferences in product suggestions