Makine Öğrenimi ile Uzun Kuyruk Ürünler için İyileştirilmiş Sonraki Öğe Önerisi

Extended Abstract – Many different types of products can be sold on electronic commerce platforms. Products can be sold regardless of where customers are. The recommendation system on these platforms plays a critical role in selecting and displaying interesting products for users. In the study, the products to be purchased next to the customers were recommended in the most accurate way. For this, machine learning algorithms were used and the results were compared. The singular value decomposition (SVD) method has achieved more successful results. Research Problem/Questions – To make the most appropriate match between an infinite number of products and many customers in the most accurate way. Short Literature Review – Many algorithms have been developed for the best solution in recommendation systems that try to persuade their customers to sell niche products, and it is currently a subject open to research. Methodology and k nearest neighbor (kNN) algorithms are in the long queue in electronic commerce have been improved by developing the next item recommendation system. Results and Conclusions – Machine learning algorithms can be used to solve problems in product recommendation systems. The SVD method suggested less erroneous recommendations for large datasets.