Hybrid Movie Recommendation System using Neural Collaborative Filtering with Gradient Decent and Association Rule Mining

In recent days, most of the people are entertained by watching movies and shows, so developing a recommendation system help people across the world in saving the time to search for high rated movies. Usually, movies are recommended based on the user interests in the different movie genres such as horror, drama, romantic and comedy. Previous researchers suggested various methods for movie recommendation using Content Based Filtering (CBF), Collaborative Filtering (CF) which failed to predict the non-linear relationship recommendation due to the availability of many genres. To overcome this challenge, we proposed hybrid model namely Neural Collaborative Filtering (NCF) with gradient decent and Association Rule Mining (ARM). Initially, MovieLens dataset is employed and then data cleaning data organization is incorporated in pre-processing. Further, the features are extracted through the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) to convert the text data into numerical data and increase the performance respectively. Finally, the developed model was measured using Root mean square error (RMSE), precision, recall, and F 1 score. Hence, the proposed hybrid recommendation model provides higher accuracy value when compared to Knowledge-aware Attentional Neural Network (KANN).