Research on the Application of High-quality Smart Classroom Education Evaluation in the Internet of Things Environment
With the upgrading of residents'entertainment methods,music recommendation algorithms based on emotional attention have been developed.The traditional music recommendation algorithm is based on the user's historical listening records,but ignores the impact of the music itself on the user's emotions.Therefore,in this study,Mel filters were incorporated into user long-term and short-term preferences,and a music recommendation algorithm was generated by analyzing the sequence time period while considering emotional attention.The research experiment was conducted on the Recom dataset,and experiments were conducted on three algorithms including random forest to verify the effectiveness of the fusion algorithm.For the experiment of discriminative ability for multiple tracks,the fusion algorithm has an accuracy rate of 97%in judging user psychology,and performs the best among the four algorithms.The experimental results show that the fusion algorithm proposed in the study has the strongest performance and is suitable for application in user preference music recommendation.
Long-and short-term preferencesMusic recommendationSequence time periodMel filter bankEmotional attention
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