Movie recommendation based on ALS collaborative filtering recommendation algorithm with deep learning model

Abstract

Development of recommender systems has recently emerged as a prominent study field that has drawn the attention of several scientists and researchers worldwide. Various fields, such as music, movies, books, news, search queries, and commercial goods, employ recommender systems. One of the well-liked and effective RS strategies is the collaborative filtering algorithm, which seeks out users who are quite similar to active one to propose products. This study suggests a unique method for recommending films based on analysis of user preference data that combines ALS collaborative filtering with deep learning techniques. The input in this case is gathered as web data based on previously performed user searches and then processed for noise reduction and normalisation. Convolutional multimodal auto multilayer graph with ALS collaborative filtering (CMAMG_ALSCF) was used to classify this processed data according to user evaluations and interests. Movies that are related to the interests of users are recommended by examining the similarity between users and other users or the similarity between movies and other movies. For several movie recommendation datasets, experimental analysis is done in terms of training accuracy, validation accuracy, RMSE, and average precision.