Research on Movie Service Recommendation Algorithms Incorporating Film Attributes and Multimodal Information

As a personalized recommendation technology, the recommendation system aims to predict users' preferences for items and provide recommendation services for users. Movie recommendation technology can help users quickly find their pre-ferred movies and thus meet their viewing needs. Traditional context-based movie recommendation models only use text data, obtaining limited information from single-modal data and failing to fully address the problem of data sparsity. This paper proposes a multimodal movie recommendation model Layered Multi-head Attention Dynamic Graph, LMADG that integrates text and image data, aiming to capture the dynamic changes in user interests and the graph structure infor-mation of user-movie interactions. By combining Graph Convolutional Network GCN and temporal attention mechanisms, LMADG can effectively extract the temporal features of users and movies and generate personalized recommendation results. Finally, comparative experiments are conducted on the Movielens-1M, TMDB, and Netflix Prize datasets, verifying that the proposed model has better recommendation quality.