A Cost-conscious Item Grouping Method for Online Shopping Services
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This paper presents an item grouping method to construct itemsets for online shopping services with the aim of minimizing itemset construction cost that consists of two elements: the number of errors and the processing time. Most existing methods focusing only on error reduction are often inappropriate to practical shopping itemset construction due to the possible high itemset construction cost caused by too much processing time for the huge number of items. For example, price comparison services should be required to handle more than hundreds of thousands of shopping items in a day. Thus, they should consider errors and processing time together in itemset construction task to reduce the overall itemset construction cost. The proposed method analyzes the trade-off between the two elements of itemset construction cost and finds the best classifier by addressing the imbalanced nature of dataset and the huge number of items. We conducted experiments using a real-world dataset of a price comparison shopping service and the experimental results illustrate that the overall itemset construction cost can be decreased dramatically by the proposed method.
Abstract
This paper presents an item grouping method to construct itemsets for online shopping services with the aim of minimizing itemset construction cost that consists of two elements: the number of errors and the processing time. Most existing methods focusing only on error reduction are often inappropriate to practical shopping itemset construction due to the possible high itemset construction cost caused by too much processing time for the huge number of items. For example, price comparison services should be required to handle more than hundreds of thousands of shopping items in a day. Thus, they should consider errors and processing time together in itemset construction task to reduce the overall itemset construction cost. The proposed method analyzes the trade-off between the two elements of itemset construction cost and finds the best classifier by addressing the imbalanced nature of dataset and the huge number of items. We conducted experiments using a real-world dataset of a price comparison shopping service and the experimental results illustrate that the overall itemset construction cost can be decreased dramatically by the proposed method.
참고문헌 (37)
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