Effective and Efficient Similarity Measures for Purchase Histories Considering Product Taxonomy
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In an online shopping site or offline store, products purchased by each customer over time form the purchasehistory of the customer. Also, in most retailers, products have a product taxonomy, which represents ahierarchical classification of products. Considering the product taxonomy, the lower the level of the categoryto which two products both belong, the more similar the two products. However, there has been little work onsimilarity measures for sequences considering a hierarchical classification of elements. In this paper, wepropose new similarity measures for purchase histories considering not only the purchase order of products butalso the hierarchical classification of products. Unlike the existing methods, where the similarity between twoelements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposedmethod can assign any real number between 0 and 1 considering the hierarchical classification of elements. Weapply this idea to extend three existing representative similarity measures for sequences. We also propose anefficient computation method for the proposed similarity measures. Through various experiments, we showthat the proposed method can measure the similarity between purchase histories very effectively and efficiently.
Abstract
In an online shopping site or offline store, products purchased by each customer over time form the purchasehistory of the customer. Also, in most retailers, products have a product taxonomy, which represents ahierarchical classification of products. Considering the product taxonomy, the lower the level of the categoryto which two products both belong, the more similar the two products. However, there has been little work onsimilarity measures for sequences considering a hierarchical classification of elements. In this paper, wepropose new similarity measures for purchase histories considering not only the purchase order of products butalso the hierarchical classification of products. Unlike the existing methods, where the similarity between twoelements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposedmethod can assign any real number between 0 and 1 considering the hierarchical classification of elements. Weapply this idea to extend three existing representative similarity measures for sequences. We also propose anefficient computation method for the proposed similarity measures. Through various experiments, we showthat the proposed method can measure the similarity between purchase histories very effectively and efficiently.
참고문헌 (17)
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