CMF-SMR: Convolutional matrix factorization for sequential movie recommendations

Abstract

Recommender system (RS) has become an essential component of e-commerce, social media, and other online platforms. Collaborative filtering (CF) is one of the most commonly used techniques in RS that relies on user-item interactions to generate recommendations. However, CF suffers from the cold-start problem, sparsity, and scalability issues. To address these challenges, this work propose a hybrid system called Convolutional Matrix Factorization for Sequential Movie Recommendations (CMF-SMR), which combines matrix factorization (MF) with convolutional neural networks (CNNs). CMF-SMR leverages the non-linear feature extraction capabilities of CNNs and the representation learning abilities of deep learning to enhance the accuracy and robustness of traditional MF-based RS. Specifically, CNNs and MF were used to respectively extract features from user-item interaction data and use them as input for learning user and item representations. The learned representations are then used to predict user-item ratings. This work evaluates the performance of our proposed method on two publicly available datasets, and the experimental results demonstrate that our method outperforms several state-of-the-art techniques in terms of accuracy, scalability, and robustness. Moreover, this work conduct evaluation metrics to demonstrate the accuracy of our proposed method. Overall, our proposed CMF-SMR provides a promising solution for addressing the limitations of traditional CF-based RS and can be applied in various domains, including e-commerce, social media, and personalized content recommendation systems.