Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (14): 118-122.DOI: 10.3778/j.issn.1002-8331.1904-0395

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Short Video Preference Rate Prediction Model with Integrated FM

WANG Limiao, XU Qinglin, JIANG Wenchao, FU Jigao   

  1. College of Computer, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2020-07-15 Published:2020-07-14



  1. 广东工业大学 计算机学院,广州 510006


Short video preference rate predictions often face a large number of users and advertisements, and the training data set is highly dimensional and sparse, which leads to a decrease in prediction accuracy. Aiming at these problems, a short video preference prediction model based on LDA-GBDT-FM is proposed. The model first uses the Latent Dirichlet Allocation model(LDA) to segment the original dataset based on the topic, and then uses the Gradient Boosting Decision Tree(GBDT) pair. The sub-training sets of different topics extract the high-impact features of continuous features, combine them with discrete features to train the Factorization Machine(FM) model, and finally effectively combine the sub-models to predict the preference rate of short videos. The experiment is based on the dataset of the Bytedance company. The experimental results show that the proposed LDA-GBDT-FM model is 3.0%, 5.7%, and 8.5% higher than the LDA-FM, FM, and LR, respectively.

Key words: short video advertisement, preference rate prediction, topic model, gradient promotion decision tree, factori-
zation machine



关键词: 短视频广告, 喜好率预测, 主题模型, 梯度提升决策树, 因子分解机