Kişiselleştirilmiş Haber Tavsiye Sistemi

Abstract

Recommendation Systems are the methods that suggest the next choices of the user in a predictable way, based on the preferences made by the user before. This method is become even more popular nowadays and it can be applied to any topic or field that needs future estimation evaluating the data at hand. It is a kind of information extraction study. Furthermore, the fact that Amazon receives about 35% of its revenue from referral systems is an indication of how important this method is. However, news recommendation system which is a similar application area, is not also widely used as others. In this study, it is aimed to design a news recommendation system by taking into account the sites the user enters, the words that they searched for and bookmarks. The machine learning model has been trained with a data set that includes news categories and news content in order to present the news to the user as interested. By giving the data from the user environment to the trained model, the found interested categories of the user is processed instantly by the RSS. These news selected from RSS are shown to the user in order of priority regarding the daily news agenda. The real user test showed impressive accuracy as 89%. This solution presents a content-based recommendation system as nature of the problem.