Commodity Management And Recommendation System Under The Context-aware Environment

With the popularization of wireless communication and the wireless mobile terminal equipments, mobile e-commerce has gradually become the focus of attention. As an important branch of logistics engineering, it combines the mobile communication technology and information processing technology, which enables the effective management of commodity. At the same time, with the rapid development of information technology and information content, the issue of “information overload” is becoming more and more serious, which brings great information budern to people.This paper discusses about the research of commodity information management and recommendation system under the context aware environment. Through pretreating of terminal sensor data, we can polymerize the information, reason and thus generate the context information. Based on further processing upon users’ preference and consumption history, it’s easy for us to grasp the users’ consuming psychology, and to provide personalized service for them. The system can also broadcast the sale information of commodity from neighbouring seller according to users’ searching habit and LBS information, which can solve the Information Island problem effectively.This system provides a mobile commodity management and recommendation system, based on context aware. LBS technology and image recognition technology are also applied in mobile terminal to transfer the shopping information into the clouds. The multiple servers in the cloud form a “commodity information cloud”, which can store and operate on the information. Users can comment on the commodity and give feedback on the goods. The cloud gives the information back based on the data mining technology. Based on Map-Reduce technology, users are divided into groups due to the clustering computing. At the same time, recommendation rules are formed, which promotes the efficiency of recommendation.A testment of the prototype system indicates that, the system can meet the actual demand of users and provide personalized recommendation for them.