基于随机森林的节目推荐优化方法
Program Recommendation Optimization Method Based on Random Forest
周立新1
作者信息
- 1. 奎屯市融媒体中心,新疆 伊犁 833200
- 折叠
摘要
为提高电视节目个性化推荐的高效性和准确性,首先进行特征工程,提取用户的历史观看记录和偏好标签等特征,通过主成分分析对特征进行降维处理.其次,采用随机森林算法对训练集进行模型训练.最后,将测试集随机分为 3 组进行实验测试,使用准确率、召回率和F1 值等指标评估模型的性能和泛化能力.实验结果表明,提出的方法能够有效地提高用户体验和推荐准确性.
Abstract
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.
关键词
节目推荐/随机森林/特征工程/主成分分析Key words
program recommendations/random forest/feature engineering/principal component analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据