Browsing by Subject "Recommendation systems"

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Open Access
Behavioral changes of the audience by the algorithmic recommendation systems inside video-on-demand platforms considering the example of netflix
(2019-05) Gürmeriç, Can
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.
Open Access
Recommendation systems as technologies of the self: algorithmic control and the formation of music taste
(Sage Publications Ltd., 2018) Karakayalı, Nedim; Köstem, Burç; Galip, İdil
The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not unique to last.fm but stems from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-a`-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension.