Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Effective and Efficient Similarity Measures for Purchase Histories Considering Product Taxonomy

  • Yang, Yu-Jeong (Dept. of Computer Science, Sookmyung Women's University) ;
  • Lee, Ki Yong (Dept. of Computer Science, Sookmyung Women's University)
  • Received : 2020.03.19
  • Accepted : 2020.07.03
  • Published : 2021.02.28
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Abstract

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2018R1D1A1B07045643).

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