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  1. Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics (MDS '12)
  2. Back-buy prediction based on TriFG
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Back-buy prediction based on TriFG
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Back-buy prediction based on TriFG

Content Provider ACM Digital Library
Author Xie, Xiaoqin Li, Li Fang, Yixiang Zhang, Zhiqiang
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.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450315463
DOI 10.1145/2350190.2350191
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Back-buy prediction Prediction model Two-way relationship
Content Type Text
Resource Type Article
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