With the rise of mobile internet and social media, instant music videos have become a key form of entertainment and information access. Traditional recommendation algorithms often fail to meet diverse user preferences, leading to suboptimal experiences. This study introduces an intelligent recommendation algorithm enhanced with an augmented reality (AR) interaction method to provide more accurate and personalized content. User behavior data is analyzed to build a recommendation model based on content and user profiles. The AR interaction method integrates recommendation results with real-world scenarios, enabling users to intuitively experience the relevance and appeal of suggested videos. In experiments comparing this method with traditional multimedia recommendation approaches, the AR-based algorithm achieved an average recommendation accuracy of 42.21%, a content diversity score of 91.86, and a user satisfaction score of 94.38. These results demonstrate that combining intelligent recommendation algorithms with AR interactions can effectively align instant music video suggestions with user preferences, enhancing engagement and platform dependency.

Xiaoyu Liu. Augmented Reality Interaction Method for Instant Music Video Intelligent Recommendation Algorithm Technology in Multimedia Background. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 9: 82-91. https://doi.org/10.25236/AJCIS.2025.080912.

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