Back-buy prediction based on TriFG

Authors:
Xiaoqin Xie;Li Li;Zhiqiang Zhang;Yixiang Fang
Affiliations:
Harbin Engineering University, R. R. China;Harbin Engineering University, R. R. China;Harbin Engineering University, R. R. China;Harbin Engineering University, R. R. China
Venue:
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Year:
2012

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Abstract

Reciprocal Relationship in twitter can be predicted by TriFG model. Based on this model, we study the extent to which the formation of a two-way relationship can be predicted in a dynamic e-commerce web site which is composed of products and customers, especially the back-buy behavior. Back-buy behavior represents a more stable interest direction of customers. Understanding the formation of back-buy behavior can provide us insights into the potential e-commerce trends and lead to more efficient advertisement. In this paper, we propose a learning framework to formulate the problem of back-buy prediction into a graphical model---Back-buy model (BBModel). Employing such e-shopping sites as Amazon as a source for our experimental data, BBModel can predict the probability of the customers back and buy the products. Finally, the experiments show that the BBModel is feasible and effective for prediction. The two-way relationship prediction can fit to the e-commerce web site.