Dijital Platformlarda Öneri Sistemlerinin Kullanıcı Deneyimine Etkisi: Netflix, Amazon Prime ve Disney+ Örnekleri

Yıl 2025, Sayı: 15, 84 - 107, 31.12.2025

Evren Günevi Uslu

https://doi.org/10.56676/kiad.1647277

Öz

Netflix, Amazon Prime Video ve Disney+ gibi küresel ölçekte milyonlarca kullanıcıya ulaşan platformlar, içerik çeşitliliği ve erişim kolaylığı ile dikkat çekerken, kullanıcı deneyimini iyileştirmek için gelişmiş algoritmalar ve öneri sistemleri kullanmaktadır. Bu çalışma, Netflix, Amazon Prime Video ve Disney+ platformlarının öneri sistemlerini ve bu sistemlerin kullanıcı deneyimine etkisini, sosyal medya, haber kaynakları, bloglar ve forumlardan elde edilen veriler ışığında incelemektedir. Çevrimiçi akış platformları, kullanıcıların içerik bolluğu içinde kaybolmaması için yapay zekâ destekli öneri algoritmaları kullanmakta, ancak bu algoritmaların etkinliği ve memnuniyet düzeyi kullanıcı deneyimini doğrudan şekillendirmektedir. Araştırmada Betimsel İçerik Analizi yöntemi benimsenmiş, Netflix için 153, Amazon Prime Video için 176, Disney+ için 176 veri noktası Mention uygulaması aracılığıyla toplanarak beş ana tema altında incelenmiştir: Genel Algı, Kullanıcı Etkileşimleri, Haber Kaynaklarında Algı, Algoritma Eleştirileri ve Popüler İçeriklerle Etkileşim. Bulgular, kişiselleştirilmiş öneri mekanizmalarının kullanıcıların içerik keşfini kolaylaştırdığını ve platformda geçirilen süreyi artırdığını ancak tekrar eden ya da ilgisiz önerilerin memnuniyeti düşürdüğünü göstermektedir. Netflix kullanıcılarının bir kısmı önerilerin başarılı olduğunu belirtirken, diğerleri sistemin kişisel tercihlere uyum sağlamakta zorlandığını vurgulamaktadır. Benzer biçimde, Amazon Prime Video’nun öneri algoritmaları da bazı kullanıcılar tarafından isabetli bulunurken, tekrarlı öneri sorunu sıkça gündeme gelmiştir. Disney+ ise güçlü markalara dayalı içerikleri öne çıkararak memnuniyet yaratmakta, fakat niş yapımların az önerildiği yönünde eleştiri almaktadır.

Anahtar Kelimeler

öneri sistemleri , kullanıcı deneyimi , dijital platformlar , yapay zekâ algoritmaları , içerik kişiselleştirme

The Impact of Recommendation Systems on User Experience in Digital Platforms: Netflix, Amazon Prime Video, and Disney+

Öz

Netflix, Amazon Prime Video, and Disney+ are global streaming platforms that reach millions of users, offering a wide variety of content and easy accessibility. To enhance user experience, these platforms utilize advanced algorithms and recommendation systems. This study examines the recommendation systems of Netflix, Amazon Prime Video, and Disney+ and their impact on user experience based on data collected from social media, news sources, blogs, and forums. Online streaming platforms use AI-powered recommendation algorithms to prevent users from getting lost in the vast content library. However, their effectiveness and the resulting user satisfaction directly shape the user experience. The study employs a descriptive content analysis method, collecting 153 data points for Netflix, 176 for Amazon Prime Video, and 176 for Disney+ via the Mention application. These data are analyzed under five main themes: General Perception, User Interactions, Perception in News Sources, Criticisms of Algorithms, and Engagement with Popular Content. Findings indicate that personalized recommendation mechanisms facilitate content discovery and increase time spent on the platform. However, repetitive or irrelevant recommendations reduce user satisfaction. While some Netflix users find the recommendations effective, others argue that the system struggles to adapt to personal preferences. Similarly, some users consider Amazon Prime Video’s recommendation algorithms accurate, but issues with repetitive suggestions are frequently reported. Disney+, on the other hand, generates satisfaction by promoting content based on its strong brand franchises, but faces criticism for the limited recommendations of niche productions.

Anahtar Kelimeler

recommendation systems , user experience , digital platforms , ai algorithms , content personalization

Kaynakça

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