Design and Implementation of Mobile Platform for Online Recommendation and Comment for British and American Literary Works

This paper first introduces the characteristics of online review information. With the help of the comment data filled in by users after browsing behavior, the user's preferences and the inherent characteristics of the product are explained by mining the description of all aspects of the product in the user's comment data. Then, combined with the topic model and collaborative filtering model, the online recommendation technology based on mobile platform is proposed, and the feature matching degree table is established. By calculating the feature matching value between the user and the product to predict the user's score on the product, to realize the commodity recommendation which is more in line with the consumers' thinking habits of network retrieval information. The experimental results on a real data set constructed on a book e-commerce mobile website show that the platform has a great improvement in the accuracy of the recommendation system compared with the traditional methods, and it has a better interpretation compared with the traditional model.