Product Recommendation System For The Wholesale Market Using Frequent Data Mining With Fp-Growth

This work presents the application of the frequent data mining technique in recommender systems and the development of an intelligent computational system, where the main objective is to improve sales in the wholesale market. Among the techniques, the FP-Growth (Frequent Pattern Growth) method stands out as an interesting algorithm for mining association rules in large sets of items. Sales of products recommended by the system were balanced, with overall average results close to 51%, cancellations of recommended products were approximately 17% and the average acceptance of suggestions was approximately 33%.