E-commerce Personalization System Based On Collaborative Filtering Technology

With the popularity of network and information technology with each passing day, E-commerce system to provide users with more and more choices at the same time, its structure has become more complex, users often get lost in a large number of goods in the information space, could not find their own need for commodities. Personalized e-commerce system came into being in such a case. Personalized E-commerce systems in E-commerce platform based on the user's information to the user behavior and recommend products to help users find the goods in order to the successful completion of the purchase process. Its wide range of applications have attracted users at home and abroad by the attention.Collaborative filtering technology is one of the earliest and most successful application of the technology in personalized E-commerce system. Its basic idea is: it found a good way for users to find the contents of his genuine interest is to find him and his interest in similar users first. Then these users interested in the content of recommended to the user. The general nearest neighbor, using the user's historical preferences information calculated the distance between the user and then use the nearest neighbor target users for goods evaluation of the weighted evaluation of target users to predict the extent of the preferences of specific commodities, so as to the basis of this system preference to the recommended target users. Collaborative filtering biggest advantage is recommended objects do not have a special request, be able to handle unstructured complex objects, such as music, movies.This paper focus on the current recommendation of the mainstream of personalized technology - Collaborative filtering technology impact of the algorithm in the light of the recommendation of quality issues and customer satisfaction impact on the recommendation of the integrity of the in-depth analysis of the issue, recommended the introduction of a combination of collaborative filtering algorithms, technology to improve the design of the paper can be achieved by the recommended strategy for the simulation. Research papers on the algorithm to improve the simulation experiments, the experimental verification, recommended improved algorithm in the accuracy, integrity, diversity and so on is better than the traditional method, especially in light of the evaluation of user data sets reflect on the recommendation of a good performance. We designed a personalized E-commerce systems framework, the completion of a common process for the reality of personalized e-commerce system provides a useful reference.