Research on Ant Colony Link Prediction Algorithm of Cross-border Social E-commerce Based on Trust Community Mechanism

In recent years, with the rapid development of information technology, social e-commerce has become the main place for users to buy goods, and effective link prediction and recommendation in social e-commerce environment has played an important role in the directions of academic circles. In this research, combining with the credit mechanism, the traditional infectious disease model is used for modeling of the trade route of Chinese cross-border social e-commerce. Based on the link prediction method, social network data sets and efficient ant colony optimization algorithm, the solution of the model in this study is realized. Under data sets from cross-border social e-commerce platforms such as Amazon, Netflix, and Facebook, TCAC-LP method has obvious advantages over other algorithms in terms of some error analysis values, accuracy, recall rate, F1 value, etc., which well reflects the high accuracy and precision of TCAC-LP method. Hence, some practical and effective suggestions for international social e-commerce trade can be provided from the perspective of customer evaluation emotion and psychological needs.