Affiliation:
1. Department of Mathematics, University of Padua, 35121 Padua, Italy
2. Department of Management Information System, Izmir Bakircay University, 35665 Izmir, Turkey
Abstract
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.
Subject
Computer Science Applications,General Business, Management and Accounting
Reference61 articles.
1. Online Store Product Recommendation System Uses Apriori Method;Fatoni;J. Phys. Conf. Ser.,2018
2. An Association Rule Algorithm for Online e-Commerce Recommendation Service;Dai;J. Econ. Bus. Manag.,2016
3. Dogan, O. (2018). Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, IGI Global.
4. Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model;Xiang;Math. Probl. Eng.,2020
5. Intuitionistic Fuzzy Originated Type-2 Fuzzy AHP: An Application to Damless Hydroelectric Power Plants;Kahraman;Int. J. Anal. Hierarchy Process.,2018
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