This paper introduces VBPR (Visual Bayesian Personalized Ranking), a scalable factorization model that incorporates visual signals into personalized ranking for implicit feedback. The model uses visual features extracted from product images via pre-trained deep networks, and learns additional layers to uncover visual dimensions that explain user feedback variation. VBPR improves personalized ranking accuracy, alleviates cold start issues, and provides insights into visual dimensions influencing user preferences. VBPR is built on Matrix Factorization (MF), which is widely used for implicit feedback. The model extends MF by adding visual factors derived from deep CNN features. These visual factors are embedded into a lower-dimensional space, enabling the model to capture visual interactions between users and items. The model is trained using Bayesian Personalized Ranking (BPR), a pairwise ranking optimization framework that maximizes the probability of positive feedback over negative feedback. VBPR outperforms existing methods on real-world datasets like Amazon clothing and Tradesy.com, particularly in cold start scenarios. It achieves significant improvements in AUC (Area Under the ROC Curve) metrics, demonstrating the effectiveness of incorporating visual features into recommendation systems. The model also reveals the visual dimensions that influence user preferences, providing insights into how visual appearance affects recommendations. VBPR is scalable and efficient, with a time complexity linear in the number of dimensions. It handles large datasets and visual data effectively, making it suitable for real-world applications. The model's ability to learn visual dimensions and incorporate them into ranking tasks makes it a valuable addition to recommendation systems, especially in scenarios where visual information is crucial for user preferences. The results show that VBPR significantly improves personalized ranking performance and addresses cold start issues, making it a promising approach for future research in recommendation systems.This paper introduces VBPR (Visual Bayesian Personalized Ranking), a scalable factorization model that incorporates visual signals into personalized ranking for implicit feedback. The model uses visual features extracted from product images via pre-trained deep networks, and learns additional layers to uncover visual dimensions that explain user feedback variation. VBPR improves personalized ranking accuracy, alleviates cold start issues, and provides insights into visual dimensions influencing user preferences. VBPR is built on Matrix Factorization (MF), which is widely used for implicit feedback. The model extends MF by adding visual factors derived from deep CNN features. These visual factors are embedded into a lower-dimensional space, enabling the model to capture visual interactions between users and items. The model is trained using Bayesian Personalized Ranking (BPR), a pairwise ranking optimization framework that maximizes the probability of positive feedback over negative feedback. VBPR outperforms existing methods on real-world datasets like Amazon clothing and Tradesy.com, particularly in cold start scenarios. It achieves significant improvements in AUC (Area Under the ROC Curve) metrics, demonstrating the effectiveness of incorporating visual features into recommendation systems. The model also reveals the visual dimensions that influence user preferences, providing insights into how visual appearance affects recommendations. VBPR is scalable and efficient, with a time complexity linear in the number of dimensions. It handles large datasets and visual data effectively, making it suitable for real-world applications. The model's ability to learn visual dimensions and incorporate them into ranking tasks makes it a valuable addition to recommendation systems, especially in scenarios where visual information is crucial for user preferences. The results show that VBPR significantly improves personalized ranking performance and addresses cold start issues, making it a promising approach for future research in recommendation systems.