关键词: 用户画像, 词向量, LDA主题模型, 随机森林

Abstract: User portrait technology can bring great commercial value to enterprises. For the user’s historical query words, the word vector can be used to obtain the expression of the query word at the semantic level, but the word vector model that generates the word vector for the same word is the same, which makes the model unable to deal with the polysemy of a word. Therefore, this paper uses the LDA topic model to assign topics to each query word, so that the query word and its topic are put together in the neural network model to learn the topical word vector. Finally, the random forest classification algorithm is used to classify the basic attributes of users and build the user portrait. The experimental results show that the classification accuracy of this model is higher than that of the word vector model.

Key words: user portrait, word vector, LDA topic model, random forest