A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher's perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company's profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners' standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.

Automated summary

E-commerce is rapidly growing, making it crucial to understand complex transactional data in order to provide practical product recommendations. Traditional methods focus on recommending frequent items, but fail to consider profitability. This study introduces a novel method called P-FARM, which mines association rules based on profitability and frequent item sets. The results show that P-FARM is a powerful tool for improving e-commerce sales and maximizing profit.