Multi criteria recommendation method of cross-border e-commerce shopping guide platform based on big data

The fit between recommended goods and user preferences determines the trading volume of cross-border e-commerce shopping guide platform. Therefore, a multi criteria recommendation method of cross-border e-commerce shopping guide platform based on big data is proposed. The user behavior is divided into three stages, Web page click, shopping cart placement and online purchase. By weighting, the user's consumption preference is obtained. The association rules of goods are mined from the two aspects of confidence and support, and then the collaborative filtering recommendation is realized based on the principles of users and goods. The test results show that the accuracy, recall and coverage of the design method are better than the comparison method and has a good recommendation effect.