DEVELOPING NEW SUGGESTIONS FOR THE CONTENTS OF A DIGITAL PLATFORM USING RECOMMENDATION SYSTEMS ALGORITHMS

In recent years, machine learning applications are being used in almost all areas of lives. The main benefits of using machine learning in marketing can be exemplified as follows; content creation, marketing budget optimization and recommendation systems. Recommendation systems are very important and useful when it comes to retaining the current customer. With the help of recommendation systems, companies can retain their customers by recommending their own products, services and contents. In this study, text mining, forecasting processes were carried out using the Netflix contents dataset shared by the data science platform called Kaggle. TfidVectorizer function was used to deal with text data while creating recommendation systems. Two different recommendation systems functions were created in this study.   While first recommendation system function performs only based on title feature of the Netflix contents dataset, the second recommendation system function performs with title, director, cast, listed_in and description features. Thanks to the results of the analysis, it is possible to evaluate the new productions on Netflix on the basis of the features of Netflix contents dataset included in the study. The proposed recommendation system functions provide greater prediction accuracy than conventional systems in data mining. Espicially the recommendation system function that has been developed secondly with the name “get_recommendation_new” uses all features in Netflix contents dataset to recommend new contents to the users.