Real Time Content-Based News Recommendation in Turkish with Apache Spark

Abstract

The readers’ behavior have partially turned into consuming news online, after news providers are transformed the printed newspapers into digital web sites. Engaging attention of the readers by retaining them on the web site contents as much as possible to consume more articles has become crucial and as a scarce resource, it is also challenging for the providers. While readers click through on the pages, system records each click for classical counting approach, also measure the time spent on pages which is critical for news providers in digital web sites. In this regard, recommendation engines which builds on the natural language processing with machine learning algorithms play important role to gain insights from the vectorized contents and provide similar articles to the readers. We motivate to analyze news content similarities expanding with tags and emotion similarities by comparing various text analytics methods results starting from sports and travel categories. By adding domain knowledge on the findings, our aim is to have a specific category based approach which could be applied on different news providers contents in Turkey.