Image Recommendation Using Clustering Techniques: A Comparative Study

The chapter explores the critical role of clustering techniques in enhancing image recommendation systems, which aim to provide personalized and relevant image suggestions to users. It begins by outlining the diverse applications of recommendation systems, from e-commerce to streaming platforms and social networks, emphasizing their importance in information management and decision-making. The text delves into the challenges of image recommendation, including object detection, segmentation, classification, and clustering, which are essential for accurate and effective image suggestions. The chapter provides a comprehensive review of state-of-the-art clustering techniques, comparing their performance across various datasets and metrics. It highlights the superiority of the K-means algorithm in handling large datasets and achieving well-defined clusters, as evidenced by silhouette scores. The chapter also discusses the practical implementation of these techniques, including data preprocessing, feature extraction using MobileNet, and hyperparameter tuning. It concludes with a detailed analysis of the experimental results, demonstrating the effectiveness of clustering in improving the relevance and personalization of image recommendations. The chapter also addresses the limitations of visual recommendation based on clustering and suggests future directions for optimizing these models.