Research on multi-product artificial intelligence recommendation
Kuotai Tang, Ting C. Huang, Zitong He, Chujun Huang · 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · 2021
With the rise of diversified stores, the relationship between consumer characteristics and commodities affects consumer orientation. Stores are in urgent need of the application of artificial intelligence, the ultimate purpose of which is to help diversified products make accurate consumption recommendations. 565 questionnaire survey data of a cultural and creative store in Fuzhou, China, this paper uses the Apriori association rule algorithm to find out the consumption relevance between goods, and then uses the C5.0 classification algorithm to set consumers' purchase decision-making behavior as the target variable, and three characteristic factors affecting consumers' basic attributes, behavior characteristics and purchase preferences as independent variables. The results showed that coffee beans (10%) were a boost to drinks (85%), tea (10%) to art tea sets (84%), wood carvers (32%) to home furnishings (84%), plants (10%) to incense burrs (80%), and toys (26%) to stationery (78%).