Integrating AI for Enhanced Exploration of Video Recommendation Algorithm via Improved Collaborative Filtering - Xiang, Yafei; Huo, Shuning; Wu, Yichao; Gong, Yulu; Zhu, Mengran (Journal of Theory and Practice of Engineering Science, 2024)
This study tackles the issue of poor user experience in video recommendation systems, primarily caused by the scarcity of user ratings and the inaccuracy of existing recommendation methods. We propose a novel algorithm that leverages Artificial Intelligence (AI) to better align with user interests and video metadata. Our approach begins by analyzing specific user behavior data to transition from traditional item rating matrices to more representative user interest matrices. We then enhance video tag analysis by incorporating a weighting factor, facilitating more precise video similarity calculations and the identification of similar video suggestions. The algorithm ultimately recommends a Top-N selection of videos to users. Through rigorous testing, including a comparison against the Movie-Lens dataset, our results exhibit a 15% increase in recommendation accuracy, underscoring the efficiency of our AI-powered method.