Optimized Model of Recommendation System for E-Commerce Website
The purpose of this work is to optimize the recommendation system by creating a new model of recommender system with different services in a global e-commerce website. In this model the most effective data sources are integrated to increase the accuracy of recommendations system, which provides the client more intuitive browsing categories interface. The sources used for this model are the user’s searching log on the global website, and data referred extracted from search engines, more clicked URLs, highly rated items, and the recommendation algorithms of new users and new items. In additions, user’s interests based on locations, and the hot releases items recommended by the admin or shop owner of the e-commerce website according to the website marketing strategy. When the users browse the website, the data sources will automatically combine to incorporate the derived structure and associate items for each category into a new browsing recommendation interface. The advantages of this model will assist the users to discover their real interested items with flexibility and high efficiency; it also provides some solutions for some serious problems and challenges that exist in the current recommendation services. Data mining technology and clustering algorithms have been proposed and applied to perform the idea of this work. ASP.NET is the implementation tool for the application website, Microsoft SQL server is used for database management.
