Abstract
With the rapid development of electronic commerce in China, a large amount of information data will be generated at every moment. How to excavate useful information is becoming an important problem In the big data age. Firstly, the smart service model of E-Commerce based on data mining was proposed, and user group mining, user interest mining, industry and domain knowledge mining and business association mining were used to bridge the gap between the big data application and requirements of smart service. Then the technical support system of E-Commerce data mining based on Hadoop platform was suggested to provide technical solution for implementation of smart service applications. And finally, the scenario knowledge recommendation service with the support of big data mining were discussed.
Highlights
In recent years, with the development of Internet, there is an e-commerce fever all over the world
The information recommendation system can recommend to users the goods they are not familiar with but are very fond of, prompting users to buy new goods, so as to improve the overall sales of ecommerce sites
IBM's A.Ballman et al developed the SpeedTracer system for data mining and analysis based on Web log[1].Paolo Buon et al proposed user information and user behavior as input to the recommendation system[2]
Summary
With the development of Internet, there is an e-commerce fever all over the world. The research of e-commerce recommendation system started earlier in foreign countries. IBM's A.Ballman et al developed the SpeedTracer system for data mining and analysis based on Web log[1].Paolo Buon et al proposed user information (called "explicit information") and user behavior (called "implicit information") as input to the recommendation system[2]. J.ben Schafer, at the University of Minnesota, and others have proposed the use of collaborative filtering to produce recommended related technologies[5]. The development of the e-commerce in our country started relatively late. Xiong Xin has improved the collaborative filtering algorithm in the personalized recommendation system and introduced the concept of stratification[8].Wu Xizhi and others put forward the association rules based on the knowledge base and the profit of the goods [9]. In the study of personalized recommendation system, Li Feng and others proposed a new personalized recommendation algorithm based on the characteristics of the goods [10]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
