Interest point is the key to improving the accuracy of e-commerce recommendation under big data environment. However, the existing predictive research ignores the comprehensive impact of various customers' behaviors and time series on interest points. In order to make up this gap, the article sets up a customer Web space and time super network model which involves four subnets:customer, time, behavior and interest point, and establishes the influence factors of behavior. Then, based on similarity of superlink prediction method and the establishment of connectivity matrix, the adjacency matrix is calculated and super triangle judgement is made, so that the most similar super edge and the best prediction results of interest points are obtained. Finally, experiment shows that the precision of interest prediction gets better with the decrease of time accuracy within the allowable range of time error. Compared with the traditional method of label prediction, the prediction accuracy is improved from 56.2% to 74%.

interest points prediction  /  Web time-space behavior  /  super network  /  superedge similarity