Analyzing Multimodal User Data for Personalized Web Content Recommendation

Abstract

The digital content explosion combined with modern user preferences requires automated, intelligent, and adaptive
content delivery systems. This work focuses on improving web recommendation systems through multimodal user
data analysis. The framework integrates text, images, behavior, and contextual information about users to enhance
personalization beyond what traditional single-modal systems offer. We created a hybrid deep learning and
collaborative filtering model integrated with context-aware and user behavior models to capture and analyze user
interactions with the system. The system was assessed using a comprehensive dataset that included various content
types and user engagement records. The analysis showed significant increases in click-through rates and user
satisfaction compared to baseline measures from other recommendation systems. This study underscores the value of
accessibility through the Internet by demonstrating how the fusion of multimodal data improves the understanding of
user intent and actions. Such understanding not only increases the accuracy of recommendations but also improves
user interaction and retention, which is beneficial for future personalization technologies on the web.