Movie-RNET: Best of Best Movie Recommendation via Deep Learning Networks from Multi-Format Data

Abstract Recommendation systems are desperately needed because of the large volume of information generated by the ever-growing use of social networks and the Internet. A recommendation system is essential since exploring the large collection can be time-consuming and difficult. In this research, a novel deep learning-based MovieRNet for best of best movie recommendation system using multi-format data has been proposed. Initially, the multiformat data such as reviews, emoji and trailer are gathered and pre-processed the reviews, emojis using normalization. The trailer videos are converted into frames and pre-processed using contrast stretching adaptive Gaussian Star filter for eliminating the noise artifacts. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Jelly fish optimization algorithm is used for decision making by analyzing the outputs of the two neural networks to get the optimal prediction rate for time-to-time by updating the user profile with past references of the users. Recall, accuracy, specificity, precision, and F-measure were some of the criteria used to evaluate the proposed technique. The accuracy of the proposed method is improved by 9.6%, 7.8%, 3.5%, and 2.15% better than the existing LSTM-CNN, SRDNet, VRConvMF and SDLM methods respectively.