A recommendation system based on fuzzy logic and machine learning for e-commerce sites

Recommendation systems are systems developed to advise on the most accurate product to users, especially on e-commerce sites, movie viewing platforms, and music listening platforms. Nowadays, the increasing number of studies on data has led to the application of different methods in recommendation systems. In the study, a fuzzy logic-based product recommendation system has been presented for users who want to buy books on e-commerce sites. Clustering has been made using unsupervised learning with information from the "also bought-viewed" book data. Six clusters were obtained by the clustering process using the K-means algorithm. The data set, which includes the page count, price and rating data, which are the other parameters of the books, has been prepared in accordance with the model. A decision tree model has been created with the data set obtained after data cleaning and clustering. The decision tree using the C4.5 method indicated the effect of the parameters to predict the book category. The rules of the Fuzzy model used in the study have been created by using this decision tree. It has been observed that successful results are obtained when tests are performed with real 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 understood that the rules can be created quickly and accurately with the help of a method such as decision trees.