<p>The development of recommendation systems represents a critical challenge in machine learning, particularly in today’s digital landscape. These systems play a pivotal role in delivering personalized product and service suggestions tailored to user preferences, thereby enhancing user experience and driving sales revenue. While methodologies such as collaborative filtering, content-based filtering, and hybrid recommender systems have been widely explored, creating a robust and effective recommendation system remains a complex and demanding task. This study introduces the DCARES model, a novel content-based filtering approach that leverages the similarity between product features. The DCARES model integrates a deep convolutional neural network (CNN) with machine learning techniques to form the core of the proposed recommendation system. CNNs are employed to extract rich and intricate features from product images, which are then combined with machine learning models to generate personalized recommendations based on user preferences. To optimize performance, the model is fine-tuned using various neural-based optimization algorithms, with the most effective one selected through rigorous evaluation. Experimental results demonstrate that the proposed model delivers highly accurate and effective recommendations in all metrics: Accuracy, RMSE, MAE and MAPE, showcasing its potential to outperform traditional approaches. By incorporating CNNs into recommendation systems, this study highlights a promising direction in the field of machine learning. The proposed approach not only enhances the quality and precision of recommendations but also opens new avenues for future research and applications, particularly in visually driven domains such as e-commerce and fashion.</p>