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A Recency Effect Hidden Markov Model for Repeat Consumption Behavior Prediction

  • Zengwei Zheng
  • Yanzhen Zhou
  • Lin SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

With the rapid development of mobile payment technology in China, people can use smartphone with some mobile payment apps (such as Alipay, WeChat pay and Apple pay etc.) to pay bills instead of paying cash. Some commercial platforms accumulated large transaction date from users’ smartphones. In the repeat consumption activities, the final few (or recency) consumption has a great impact on current consumption than long-ago consumption. But traditional HMM can’t deal with this recency effect in our repeat consumption case. This paper proposes a modified HMM method based on recency effect to predict the users’ repeat consumption behavior. We introduce a factor to represent the different recency effect of different time distance. An empirical study on real-world data sets shows encouraging results on our approach, especially on the consumer group which has the most uncertain consumption behavior.

Keywords

Hidden Markov Model Recency effect Repeat consumption 

Notes

Acknowledgment

This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY17F020008).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hangzhou Key Laboratory for IoT Technology and ApplicationZhejiang University City CollegeHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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