Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness of factorization machines for recommendation tasks. The present work introduces a novel hybrid deep factorization machine (FM) model, referred to as ConvFM. The ConvFM model use a combination of feature extraction and convolutional neural networks (CNNs) to extract features from both individuals and things, namely movies. Following this, the proposed model employs a methodology known as factorization machines, which use the FM algorithm. The focus of the CNN is on the extraction of features, which has resulted in a notable improvement in performance. In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. This paper presents the experimental procedures and outcomes conducted on the Movie Lens dataset. In this discussion, we engage in an analysis of our research outcomes followed by provide recommendations for further action.