Prediction of Movie Ratings Based on Interactive Attribute Enhancement
Movie rating prediction aims to predict the possible ratings that users may give to unreviewed movies,and is an important basis for practical applications such as recommendation systems and movie classification.Existing prediction methods mainly focus on the representa-tion of interaction information and text information between users and movies,with less consideration given to the direct representation of attri-bute features.To this end,a movie rating prediction model based on interactive attribute enhancement is proposed.Firstly,consider using the embedding vectors of attribute nodes in the network to represent different attribute feature information.Construct a movie information network based on the interaction and dependency relationships between data,and use the Metapath2vec algorithm to obtain the embedding vectors of attribute nodes.Convert each attribute feature into vector representations with different meta path structure information and semantic informa-tion.Then,the attribute feature vectors of users and movies are inputted into the two-tower model and interactively fused with their respective ID feature vectors to explore the impact of different attribute preferences on users and movies.Finally,the user and movie feature vectors are obtained,and the user's rating prediction for the movie is achieved through dot product.The results on public datasets indicate that the pro-posed model has higher prediction accuracy compared to traditional models,demonstrating the effectiveness of the model.
movie rating predictionMetapath2vectwo-tower modelinteractive attribute
国家科技期刊平台
NSTL
万方数据
维普
