YTBlend: YouTube Video Recommendations System

In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. In this research, a sophisticated smart system for learning video recommendations is proposed at the intersection of multimodal data mining and data synthesis in online education. The approach aims to enhance personalized and effective learning experiences by transcending the limitations of traditional recommendation systems. Innovative data synthesis techniques are employed to expand the dataset and improve the adaptability of the system, integrating diverse data sources including text, images, and videos. The methodology involves a comprehensive analysis of learning materials using cutting-edge technologies like natural language processing, computer vision, and video content analysis to deepen the understanding of learner-content interactions. Furthermore, the challenge of hashtag recommendation in short videos is addressed by leveraging their multimodal nature, combining sentiment and content features from image, audio, and text modalities to enhance the accuracy and rationality of hashtag recommendations. This research contributes to redefining the landscape of intelligent learning video recommendations, offering a dynamic platform designed to continuously adapt to the evolving needs and preferences of learners.