Research On Personalized Recommendation Algorithm Based On Review Text

In the context of today’s big data era,the massive Internet data and information cannot be effectively utilized,and the adverse impact of "information overload" is increasingly aggravated.The recommendation system can effectively build models for users,thus filtering Internet information for users,and greatly alleviating the adverse effects of "information overload".Traditional recommendation systems only extract information from the user’s historical rating data when modelling the user,inferring the user’s preferences and making recommendations for the user.However,with the increasing amount of data in the Internet,such as the number of newly registered users and the variety of products in online shopping platforms,the cold start problem is serious and the sparsity of the user’s historical rating data is increasing,which greatly affects the recommendation performance of traditional recommendation systems.In order to solve the above problems,the most common method is to introduce other auxiliary information,and the user’s text review information represents the user’s subjective evaluation of the item,containing a large amount of user information and item information,so in recent years,the user’s historical review information is widely used in various recommendation systems,but how to mine more and more personalized user and item information from the user’s historical review text is the current various However,how to mine more and more personalized user and item information from the user’s historical comment text is an urgent problem for recommendation systems.In order to obtain better recommendation performance,the main research of this paper is as follows.1.Introducing the attribute information of users and items,using the attribute similarity of users and items to achieve recommendations for new users and new items,alleviating the impact of cold start on recommendation performance.2.The algorithm model uses two parallel convolutional neural networks to extract text features for users and items respectively,and introduces a three-level attention mechanism in the algorithm model to extract more personalized features for users and items from word level,statement level and comment level respectively.Most of the current recommendation algorithm models based on review text lack the interaction features between users and items in the text feature extraction process,so this paper uses two parallel convolutional neural network models and introduces a common attention network in the model to simulate the interaction between users and items in order to mine more finegrained user and item features.3.In order to verify the effectiveness of this paper’s algorithm model in improving the recommendation effect,this paper set up comparison experiments on five datasets from different areas of Amazon,and the experimental results show that this paper’s algorithm performs best in the recommendation effect;in order to verify the role of this paper’s main research content in improving the recommendation effect,this paper set up ablation experiments,and the experimental results show that this paper’s research content all help to explore the text data features of users and items,so as to achieve the role of improving the recommendation effect.4.A personalized movie recommendation system is designed and completed with the algorithm model as the core of the system,and the system can run stably through setting system test.