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

Abstract:

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