E-Commerce Personalized Recommendation Model Based on Semantic Sentiment

Abstract

The real economy has moved to online electronic market transactions as a result of the rapid development of Internet technology. Online shopping makes up a growing portion of transactions in China's e-commerce market, and the number of users who are aware of online payment transactions on mobile phones is rising. Online shopping platforms like Taobao and JD.com, which are all exemplary online shopping platforms, are constantly emerging. However, because there is so much product information available when shopping online, it can be challenging for users to locate the information they need. Recently developed personalized recommender systems have successfully addressed this issue. The system can predict the user's preferences through extensive data analysis, and it then pushes the predicted information to the user interface, greatly increasing the user's purchasing efficiency and the advantages of e-commerce. As a result, in the modern era, research on the personalized recommendation model in e-commerce has become increasingly popular. In this study, semantic sentiment analysis - which is improved on the traditional semantic sentiment analysis algorithm - is introduced in the research of a personalized recommendation system, and 1000 users are chosen for an experimental study. On the user's personalized product recommendation, the improved semantic sentiment analysis and other widely used personalized recommendation algorithms are compared. According to the survey results, the average transaction success rate is 71.3 percent, and the maximum search time is 1.74 milliseconds when collaborative filtering recommendation algorithm is used. Semantic sentiment analysis has reduced search times to a maximum of 1.42 milliseconds and increased transaction success rates to 87.9 percent. After the addition of semantic sentiment analysis, it is clear that the personalized recommendation system model has a higher accuracy in recommending the products that users have expressed an interest in, which can have a greater positive impact on e-commerce transactions.