基于商品描述文案的点击预测模型

In order to predict the impact of commodity characteristics on click in commodity description copy, quantitatively analyze users' consumption behavior characteristics and alleviate the cold start problem, this paper established a click prediction model based on LDA model and text emotion analysis. Based on the LDA topic model, the model classifies and screens the commodity description words, analyzes the emotion of the constituent words, constructs the feature vector to represent the user's emotional tendency to the characteristics of the commodity, and predicts the click through the lightgbm algorithm. The model can transform unstructured text data into structured data, quantify users' interest in different characteristics of goods, and use the similar characteristics of different goods to alleviate the cold start problem. The experimental results show that the model can effectively improve the click prediction effect and alleviate the cold start problem.