A Book Recommendation System Using Decision Tree-based Fuzzy Logic for E-Commerce Sites

Recommendation systems are systems developed to advise users on the most accurate product, especially on e-commerce sites, movie viewing platforms, and music listening platforms. Nowadays, the increasing number of studies on data is led to applying different methods in recommendation systems. A fuzzy-logic-based product recommendation system is proposed for users who want to buy books on e-commerce sites in the study. Clustering is made using unsupervised learning with information from the “also bought-viewed” book data. A decision tree model is created with the data set. The rules of the Fuzzy model used in the study are created by using this decision tree. It is observed that successful results are obtained when tests are performed with actual data and decision trees, and fuzzy models are used together. Usually, in fuzzy models, data is not required. It is necessary to know the parameters and their effects during the design of the model. However, it will be complicated to determine rules in complex and challenging models like as in this study. As a result of the successful results obtained in this study, it has been shown that the rules are created quickly and accurately with the help of a method such as decision trees.