Study Of Personalized Recommended Engine Based On Consumer Interest Degree Model

With the rapid growth of the Internet, huge amount of information not only bring the convenience to users, but also increase the workload for users to find their really wanted information. In the area of E-commerce, how to help users to find the goods they are really interested with efficiency and convenience, is the key to meet the needs of users better and gain more profit. Personalized recommendation system is a good choice to accomplish this task well. Website with personalized recommendation system can improve the users purchase satisfaction limit and foster their loyalty to the website, which can make more benefit for enterprise.This paper introduces the e-commerce technology, personalized recommendation system and user modeling method, and then describes the role of data warehouse in E-commerce, finally studies personalized recommendation system deeply from the point of interest degree model.The recommendation algorithm based on user interest is one of the common methods used by E-commerce personalization recommendation engine, but consumer purchase interest can not be described accurately by the existing user interest recommendation algorithms. Thereby, this paper presents a personal interest degree model based on consumer behavior, and consumer behavior weight is introduced. The algorithm describes user’s interest degree model from a different view--browsing behavior, for this reason, the recommendation result is more satisfied user’s needs, reflects the different characteristics of consumer goods different consciousness.By further study the author found that the existing personal interest degree model based on consumer behavior and recommendation algorithm has not take user’s interest of goods which is changing through time into account. In order to solve this problem, this paper presents a personal interest degree model updating algorithm based on consumer behavior feedback which adapts user’s recent interest and user’s history interest to the algorithm.This algorithm improves the result more exactly.To verify the proposed personalized recommendation algorithm feasibility and correctness, in this paper, experimental model is constructed, and realized proposed recommendation algorithm by Java. Through the experiment, the proposed algorithm and model achieves the desired effect...