A Hybrid Movies Recommendation System Based on Demographics and Facial Expression Analysis using Machine Learning

Abstract

Cinemas and digital platforms offer an extensive array of content requiring tailored filtering to cater to individual preferences. While recommender systems prove invaluable for this purpose, conventional movie recommendations tend to emphasize specific attributes, leading to a reduction in overall accuracy and reliability. Notably, the extraction process of facial temporal attributes exhibits a suboptimal level of accuracy, thereby influencing the classification of attributes and the overall accuracy of the recommendation system. This article introduces a hybrid recommender system that seamlessly integrates collaborative filtering and content-based methodologies. The system takes into account crucial factors such as age, gender, emotion, and genre attributes. Films undergo an initial categorization based on genre, with a subsequent selection of the most representative genres to ascertain group preferences. Ratings for these selected movies are then predicted and organized in descending order. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, the system achieves real-time extraction of facial attributes, particularly enhancing the accuracy of emotion attribute extraction through sequential processing. The CNN model demonstrates a commendable 55.3% accuracy score, the LSTM model excels with a 59.1% score, while the combined CNN and LSTM models showcase an impressive 60.2% accuracy. The performance of the recommendation system is rigorously evaluated using standard metrics, including precision, recall, and F1-measure. Results underscore the superior performance of the proposed system across various testing scenarios compared to the established benchmark. Nevertheless, it is noteworthy that the precision of the benchmark marginally surpasses the proposed system in the age groups of 8-14 and 15-24.