Research And Application Of Recommendation Algorithm Based On Deep Learning

With the interactive-highly development of computer technology,although it brings a lot of convenience and solves the needs of users,it also occurs the information overload problem.How to quickly find the information of interest fro m these huge information has became important,and personalized recommendation has became more popular.The e-commerce platform usually uses the record of the user to purchase the goods at ordinary times,the portal website often uses the category of the user browsing the news to recommends relevant information,the entertainment industry always analyzes the type of the user watching the movie to Attract users' attention.The current collaborative filtering recommendation algorithm is typically partitioned according to a user dimension,an item dimension,or a model of deep learning.Although it has been widely used,which still has low recommendation accuracy,cold start of new items and so on.So this paper aims to improve the accuracy of the recommendation algorithm by using a deep learning model.The main work of this paper: introduce the principle of traditional recommendation algorithm(based on user collaborat ive filtering algorithm,item-based collaborative filtering algorithm,collaborative decomposition algorithm based on matrix decomposition),and experiment with above algorithms based on 1M's movielens dataset.It is found that the accuracy and coverage of the user-based collaborative filtering recommendation algorithm and the item-based collaborative filtering recommendation algorithm are relatively low,and founds that the matrixbased collaborative filtering algorithm which's deviation between the prediction result and the actual value is large.With the rise of deep learning in recent years,the combination of deep learning model and collaborative filtering algorithm is adopt ed.It has also become more and more popular.In order to solve the above problems,the paper first introduces how to combine the restricted Boltzmann machine model wit h the recommendation algorithm,and proposes a method for extracting data features —setting thresholds to extract data features,and then given a new K-Item RBM recommendation algorithm.Finally the algorithm model is trained and predicted with the extracted features.The error between the predicted data and the real data can be reduced through the above K-Item algorithm by the experimental analysis and the performance of the recommendation system has been improved.In addition,in order to improve the accuracy of recommendation,this paper introduce an improved CNNCF neural network recommendation algorithm,which uses convolutional neural network to extract the text data in the dataset,then trains the algorithm model,and finally makes personalized recommendations for users.Through the comparative analysis of the experiment,the accuracy and coverage of the recommendation are significantly improved.In the end,with making lots of the subtle analysis and demand research of the movie recommendation website in the entertainment industry,based on the clearing of the requirements and the core recommendation algorithm,we realize the overall framework design and database design of the film recommendation application,and finally complete a recommendation algorithm application based on deep learning.