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Increasing recommended effectiveness with markov chains and purchase intervals

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Abstract

Recommendation system is an important component of many websites and has brought huge economic benefits and challenges for online shoppers and e-commerce companies. Existing recommendation systems focus on producing a list of products which users may be interested to purchase, while overlooking the purchase chain and temporal diversity which may increase the likelihood of a purchase decision. In this paper, we propose to utilize the Markov chain to track the chain of users’ purchase behaviors and utilize the purchase intervals to improve the temporal diversity for e-commerce recommender. We design and implement several algorithms and integrate these into our recommendation model. We evaluate our system on a real-world e-commerce dataset. Experimental results demonstrate that our approach significantly improves the accuracy, conversion rate and temporal diversity compared to the state-of-the-art algorithms.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Wanrong Gu, Shoubin Dong & Zhizhao Zeng

Authors
  1. Wanrong Gu
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  2. Shoubin Dong
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  3. Zhizhao Zeng
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Correspondence to Shoubin Dong.

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Gu, W., Dong, S. & Zeng, Z. Increasing recommended effectiveness with markov chains and purchase intervals. Neural Comput & Applic 25, 1153–1162 (2014). https://doi.org/10.1007/s00521-014-1599-8

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  • DOI: https://doi.org/10.1007/s00521-014-1599-8

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