The Design And Implementation Of An Insurance Recommendation System Based On E-commerce Platform

With the rapid development of the information technology and network, e-commerce is booming. People step into the big data times from the era of information scarcity gradually. Information is so much and the complexity of searching information have become the bottleneck of the development of the e-commerce. In the new times of the information technology, both producers and readers, are overwhelmed by so many choices. Producers need to deliver targeted advertisements, while readers need to find products that meet their expectation. However, recommendation system is the important tool to deal with this contradiction.Recommendation system, by definition is used to recommend some specific products to specific users in e-commerce. It is a software system which is based on the customer’s personal attributes, personal likes, buying habits to recommend appropriate products to specific customers. However, this paper is based on insurance recommendation which is an empty page in the world. If we use original algorithms, in the circumstance of lack of users’attributes and buying or browsing behavior, it will be very difficult to recommend appropriate products to users and the target of recommending is lost.Firstly, we investigate the existing domestic and international main e-commerce recommendation system. Based on this, we analysis the status of the X e-commerce insurance website and requirement of the development of the personalized recommendation system. And then we devised a system framework, which includes implementation layer, the engine layer and the data/knowledge layer. Secondly, in the detailed design stage of recommendation system, we quantify the customer attributes and product attributes. According to each recommendation algorithm, we designed the schema of the database, which includes the raw data tables and the result table. Importantly, we designed four recommendation algorithms that is suitable all kinds of scenes and customers. These algorithms include the statistical recommendation algorithm, the association rules recommendation algorithm, the user-based collaborative filtering recommendation algorithm, the item-based collaborative filtering recommendation algorithm, the content-based recommendation algorithm. And then in order to verify the correctness of the algorithm, we tested these algorithms. Finally, we devised a system deployment technology solution to interchange with the X insurance e-commerce website.