A Personal Interest Degree Model Updating Algorithm Based on Consumer Behavior Feedback
Xiaoming Li · Journal of Liaoning University · 2011
Personalized recommendation is a widely applied technology in e-commerce.Since the existing user models can not renew in time from the consumer interest changing,the paper presents a personal interest degree model Updating algorithm based on consumer behavior feedback,which analyzes consumer purchase history and consumer behavior mode,and updates the consumer interest automatically from the user browsed contents to get the recommended list.On this basis,the algorithm can predict the personal intererst order of recommended commodities from the purchase history,which can be used to make the personalized recommendation for each user.Experimental results show that the algorithm can identify user personalization interest more efficiently,since it can improve the recommendation accuracy and customer satisfaction.