Abstract: Aiming at the problem of how to quickly extract accurate user interests from massive short video data,user data and interactive data,a three-level label user portrait construction method based on topic model is proposed.Based onthe topic construction method,the video topic words obtained by the fused LDA and GSDMM topic models are used as user interest expression vectors.Firstly,an LDA filter is built to eliminate the topic-independent text information by comparing the threshold,so as to reduce the scale of the text and reduce the influence of non-main corpus on the generation of interest expression vector.Then,the construction method of the feature word weight matrix combining semantic information and context information is proposed.The Bi-GRU neural network is used to calculate the context feature of the word vector as the context feature,and the word frequency weight calculated by the TF-IDF algorithm is used as the semantic feature.Combining context and semantic features to expand the meaning of feature words.Finally,the GSDMM model with interest weight distribution is used to learn the feature vector weight matrix,and the user interest tag generation and the interest weight correction under the influence of different user preferences are realized.Experiments show that this method can represent user portraits more completely and accurately,which is better than single topic construction method,and performs well in clustering effect.By constructing a complete user portrait,the user’s pain points could be accurately grasp,so as to provide services for subsequent personalized recommendation.

Key words: Short video, User portraits, Topic analysis model, Semantic weight, Context weight