Design And Implementation Of Recommendation System Based On Analysis Of User Comment Concern

With the rapid and steady development of e-commerce in my country,the types of online shopping products are becoming more and more refined,logistics and distribution are becoming more convenient,and consumers are more willing to make online shopping.People gradually abandon the traditional large-scale physical shopping store consumption mode and turn to new online shopping stores.After consumers purchase and use a product,they will make user ratings and product reviews on the product to express their shopping experience.At the same time,when people make purchases,they often first check the product review information to get an understanding of the product.User reviews usually include a digital score and text comments,these information content reflects the user's preference for different product attributes and user emotional tendencies.Recommendation systems have been widely studied and applied in electronic commerce.All e-commerce platforms provide the option of "Guess what you like".At this stage,people do not lack channels for collecting information.Instead,how to make personalized recommendations under the circumstances of information overload has become the key.The core of the recommendation system is to use a personalized algorithm to use the user's evaluation of different products to dig out their interests and preferences.This paper uses BERT pre-trained language learning model,BLSTM network model and Attention drive mechanism model to construct a deep learning model to analyze the preference of user comment text.First,build a web crawler tool to collect user comment information on product pages,preprocess the text data and store it in an operational database.Then use BERT to pre-train the comment text,use BLSTM to extract two-way context features,and then use the attention mechanism to assign information weights.Obtain user comment attention points,and then generate personalized recommendations for different users based on the user's interest preference model.The recommendation effect is more accurate than traditional recommendation systems.