Abstract
With the rapid development of the Internet era, online sales services have gradually become an indispensable part of people's daily life. In the face of huge network data, how to find products that can maximize the utility of customers and form product recommendations that are beneficial to merchants has become an important issue. In this paper, through the in-depth analysis of the commodities and customer purchase data of a bicycle commodity store, the Apriori algorithm is used to mine the association rules of the data, and the commodity combination with strong correlation is obtained. At the same time, the traditional Apriori algorithm has certain limitations, that is, the algorithm only considers the probability of the transaction, but does not consider the weight of different products, and then we introduce the intuitionistic fuzzy number to represent the weight of the item. Improvements have been made, resulting in a more accurate and effective product recommendation combination. The implementation of this algorithm can be widely used in the commercial field to achieve the purpose of using best-selling commodities to drive relatively non-selling commodities, and at the same time maximize the utility of consumers, which also brings greater benefits to businesses, thus forming a healthy network. The sales structure has important practical significance to the current era.
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