Research And Application Of User Profile In Recommendation System

With the rapid development of the Internet,online shopping has become the new normal.In order to allow users to quickly find the required products,and at the same time make sellers quickly locate people who are interested in their products,the recommendation system has become one of the excellent solutions.At the same time,the user profile as an abstraction of the overall picture of the user's information,can more quickly and comprehensively analyze the user.This paper studies the construction technology of user profile on e-commerce websites,and focuses on the establishment of labels and the optimization of label weights in user profile technology.A text sentiment analysis model based on BERT is designed for non-quantitative users in user profile technology.Analyze the information,and adjust the user profile in combination with the interest-forgetting curve.In the research on the construction of user profiles,there is relatively little research on non-quantitative information.But sometimes non-quantitative information can better reflect the user's true preferences,such as user comment information.The user's comment information mainly exists in the form of short text.Therefore,this paper designs a text sentiment analysis method based on BERT to analyze the user's comment information,get the user's true sentiment for this product,and preliminary tune the user's profile based on the analysis results.Most previous studies have overlooked that the user's interests and hobbies will change with time.Therefore,this paper designs the interest-forgetting curve in conjunction with the memory-forgetting curve,and tunes the user's profile based on the curve to make the user's profile more accurate.Finally,this paper uses the data scene of an e-commerce website and the above methods to design a construction scheme for user profiles,and develops a corresponding recommendation system.The system realizes the construction of userprofiles,and at the same time combines the BERT-based text sentiment analysis model and the interest-forgetting curve to optimize the user profiles.According to the tuned user profile,it provides users with personalized recommendation function and verifies the rationality of the user profile through the recommendation results.