Program Recommendation Optimization Method Based on Random Forest
To improve the efficiency and accuracy of personalized TV program recommendations,feature engineering is first carried out to extract features such as user's historical viewing records and preference labels.Principal component analysis is used to reduce the dimensionality of the features.Secondly,the random forest algorithm is used to train the model on the training set,and the test set is randomly divided into three groups for experimental testing.The performance and generalization ability of the model are evaluated using indicators such as accuracy,recall,and F1 value.The experimental results show that the proposed method can effectively improve user experience and recommendation accuracy.
program recommendationsrandom forestfeature engineeringprincipal component analysis
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