Design And Implementation Of Personalized Movie Recommendation System Based On Hybrid Recommendation Algorithm

The recommendation system is essential to maintain the user’s participation and satisfaction with personalized recommendations in the era of information explosion.Users expect to obtain personalized content in modern e-commerce,entertainment and social media platforms,and platforms expect to provide users with items that meet their needs to increase user loyalty.With the exponential growth of network data,the drawbacks of traditional recommendation techniques have become increasingly prominent.With the continuous development of the deep learning technology,its value in the field of recommendation technology has been continuously explored.In order to alleviate the impact of sparse user interaction data and cold start of new projects on recommendation performance,deep learning technology is integrated with the recommendation system to better tap the potential interests of users and improve the accuracy of system recommendation and user loyalty.A hybrid recommendation algorithm is proposed,which makes full use of the explicit information of users and items by using deep learning.For attribute information,the deep neural network is used to extract user and item features;for item text information,a convolutional neural network with a self-attention mechanism is used to extract item text features,and then user features and movie features are input into the neural matrix decomposition model.With the help of a general matrix factorization model to learn low-level user item relationships,a multilayer perceptron learns high-level user item relationships,and the output of the model is the predicted score.Finally,the algorithm is verified on the Movieens-1M dataset and compared with a variety of algorithms,which can effectively alleviate the impact of sparse interactive data and project cold start problems on the recommendation effect,and improve the accuracy of scoring prediction.Based on the research and realization of the hybrid recommendation algorithm,a personalized movie recommendation system is constructed.From the user’s point of view,the demand analysis and functional design of the movie recommendation system are carried out,and functions including user registration,login,personalized recommendation,regular recommendation,movie collection and scoring are realized.At the same time,considering the management of data information in the recommendation system,a back-end data management system was designed to realize functions including user management and movie management.Applying the hybrid recommendation algorithm to the movie recommendation system to make personalized recommendations for users can alleviate the "information overload" phenomenon of network data,fully explore user interests,and have certain use value and research value.