The Application of Artificial Intelligence in Radio and Television Content Recommendation System
This article explores the application of artificial intelligence in radio and television content recommendation, and focuses on the optimization of user profile construction methods and content-based recommendation methods. Firstly, for the construction of user profiles, techniques such as matrix factorization were used to transform user behavior into feature vectors. Secondly, for content-based recommendation methods, algorithms for similarity calculation and optimization objectives have been introduced. Finally, experimental validation was conducted using the Last.fm dataset. The experimental results show that the proposed method can effectively improve the recommendation accuracy and personalization level of the radio and television content recommendation system.
万方数据
维普
