Behavioral changes of the audience by the algorithmic recommendation systems inside video-on-demand platforms considering the example of netflix
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Digital media revolution has provided new possibilities for different industries globally, in lives of individuals including business related areas, entertainment and personal relations. Technological advancements have sped up these changes, especially with the Internet and computational devices. Convergence of the Internet and existing media elements like telephones and computers have altered media, society and the culture of 21st Century. Traditional media forms like television has started to be affected by these changes as well. Streaming platforms have emerged over the last decades that are providing unlimited access to video or music material for monthly subscription fees. Netflix is one of these platforms which uses algorithmic recommendation systems that analyze user behavior for generating relevant material for each user. Recommendation systems are fundamental elements inside Video-on-Demand services that provide a unique experience for each user. With studies around the world, effects of Video-on-Demand services is being researched over the years, such as its effect on binge-watching behavior among the users. This thesis aims to focus specifically on the effects of algorithmic recommendation systems on the watching behavior of the audience of Video-on-Demand platforms by focusing on Netflix. As to analyze these possible effects, a theoretical background is presented that focuses the steps of these changes happening in the digital media era; followed by a research study conducted by Turkish Netflix subscribers through detailed interviews. Two main behaviors are observed over the study, which are presented in detail within the findings.