Development of Machine Learning Based Product Collection Creation and Recommendation System
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Abstract
E-commerce has become increasingly popular in recent years and is growing at a dynamic pace. In this sector, maximizing customer satisfaction and increasing sales volumes are of great importance. Developing sales strategies aligned with customer interest is crucial. The aim of this study is to develop a system that enables the creation and recommendation of collections using machine learning-based methods. To achieve this, models were developed using machine learning techniques including DT, LR, XGBoost, LightGBM, RF, and ANN. These models were designed to predict the popularity of collections, rank them on product detail pages and homepages, and provide personalized collection rankings. The performance of the developed models was evaluated using the metrics R^2, MSE, and MAE. The results indicate that the XGBoost Regressor model demonstrates the most successful prediction performance. The developed system led to a 5% increase in the time spent by the customers on the application and a 10% increase in the number of product clicks.
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