Cross Domain Movie Recommendation System using Personalized Preference Transfer

Abstract

Recommendation System has become more customized than ever. However, it suffers from cold start and data sparsity issues. Cross Domain Recommendation (CDR) has been proposed as a promising and feasible approach which yields recommendations in the destination domain based on the characteristic and personalized preferences of the users in the source domain. Since the fondness varies for each user, their taste bridge would also be different. Most of the well-known existing models make use of common preference bridge when it comes to transferring preferences for all the users in the system. Personalized Preference Transfer (PPT) for personalizing user preferences using Characteristic Encoder (CE) where user characteristic is mapped to initializing maps is pro-posed for cold start users and from books for warm start users. In this model, a Meta net-work with user attributes is used to attain the bridge preferences for individual users. The Deep Neural Network (DNN) which is a neural network model is used to provide optimized recommendations to the users. The accuracy and efficiency of personalized user preferences transfer is determined based on the comparisons from different models and train/test ratios, making use of two evaluation metrics MAE (Mean Absolute Error) and RMSD (Root Mean Squared Deviation).