Stratified Advance Personalized Recommendation System Based on Deep Learning
Arpit Deo, Riya Jaisinghani, Sagar Gupta, Safdar Sardar Khan, Adish Soni, Kushal Gehlot · Ingénierie des systèmes d information · 2023
A recommendation system is a refinement system that uses massive amounts of data to forecast and present user-preferred products.We employ web log files, including previously searched data, and browsing history, and transmit it to a SoftMax model in our recommendation model.We also use this data to perform user behaviour analysis using the k means clustering algorithm.Furthermore, we transfer users' feedback data, which is divided into explicit and implicit data, to the EIMNF model, which is a neural matrix model that aids us in forecasting users' preferences.Furthermore, we undertake cross domain analysis with the use of a CNN FT model, and all of the outputs created by the algorithm are referred to as intermediate recommended items, and they are delivered to an item pool to be reranked.Re-ranking improves accuracy and allows us to provide the best possible suggestions to our users.We employ a graph neural network to execute re-ranking, and the best things generated after reranking are provided to our end-user.We compared our model to various models and found that proposed model has 0.91 precision, 0.84 recall, 0.87 F-Measure and holds 91% accuracy.