Paper Title
Restaurant Recommendation using Deep Learning and Clustering

Abstract
This paper presents a novel approach for restaurant recommendations based on food images, integrating deep learning and clustering techniques. Leveraging the VGG19 convolutional neural network (CNN), the proposed methodology focuses on feature extraction from food images, subsequently utilizing KMeans clustering to group similar food items. The process entails preprocessing food images, extracting high-level features with VGG19, and clustering them in a latent space defined by the extracted features. Experiments conducted on a comprehensive dataset of food images assess the effectiveness of the approach, evaluating its ability to accurately classify food items and generate meaningful clusters. Results indicate the efficacy of the proposed method in providing relevant restaurant recommendations based on the visual similarity of food items. Additionally, the implications of these findings and potential avenues for future research in the domain of food recommendation systems are discussed. This research contributes to the advancement of personalized recommendation systems, highlighting the potential of combining deep learning and clustering techniques to enhance user experience in restaurant recommendation platforms. By capitalizing on visual cues inherent in food images, the proposed approach offers a promising avenue for improving the accuracy and relevance of restaurant recommendations, ultimately enriching user interactions and satisfaction in recommendation systems. Keywords - Restaurant recommendation, food images, deep learning, VGG19, KMeans clustering, feature extraction, image classification, personalized recommendation systems, machine learning, food recognition.