Purpose:
Netflix is a subscription-based streaming service that allows members to watch Tv shows and movies. Depending on your subscription plan, you can download Tv shows and movies to your IOS, Android, or Windows 10 and watch without an internet connection. Throughout my semester have obtained some bit of knowledge about Netflix, on how its recommendation algorithms work, by surveying what the users are watching in a current base and recommend them another entertainment, that is related to what there watching. And I know they also use thumbnails to attract their users to watch a show or a movie that they have never seen before, which is called personalized auto-generated thumbnail images. 

Hybrid Recommendation Algorithm for Personalization of Customer Experience:  

From the Abstract of this article topic and how Netflix is related to this, is by the recommendation system examines a large data collection and focuses on providing the user with reliable content recommendations. There are a variety of recommendation systems in use today, including Netflix, and other huge platforms, I.e., YouTube, Tinder, and Amazon, to name a few. Now there is two major things that the hybrid recommendation algorithms for Netflix, and there are a collaborative and Hybrid filtering. According to a database source for collaborative filtering, which is another for designing a recommendation system. It works by analyzing similarities between the interactivity of users. This interactivity of the user is recorded with the help of either explicit opinion or implicit opinion, but on the other hand the explicit opinion is recorded by keeping track of user rating whereas implicit opinion is recorded by keeping track of user likes, clicks, and visits. Common examples of RS (Reporting) where CF (compare) is used include platforms like Netflix and other ones like Spotify, YouTube, etc… And for hybrid filtering, which is a combination of multiple recommendation techniques to overcome individual drawbacks and benefit from its complementing advantages. But its system can be implemented either by combining individual predictions of content-based and collaborative filtering or by enumerating capabilities of content-based filtering to collaborative filtering or simply by consolidating both techniques together. One of the best examples of a hybrid approach of recommendation is used by the most popular movie streaming application i.e. Netflix. Netflix makes use of a combination of collaborative and content-based approaches. It applies collaborative filtering to track user interest by maintain watch and search history and utilizes it to recommend movies watched by similar users. It uses a content-based approach to recommending highly rated movies having similar characteristics. 
Article/citation link: S. Bohra and M. M. Bartere, “Hybrid Recommendation Algorithm for Personalization of Customer Experience,” 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022, pp. 1-9, doi: 10.1109/I2CT54291.2022.9824545.

Netflix recommendation/ Academic Adar measuring: 
According to a database website, on how Netflix algorithms work is recommendations, building a content based on a recommendation engine model, using Academic Adar Measuring. From an article published by IEEE (Institute of Electrical and Electronics Engineering) form 3 different authors Abdullah Havolli; Arianit Maraj; Lorik Fetahu, showed a Netflix study case. So, Academic Adar Measuring is used to build a recommendation engine on a graph. They use an undirected graph to show the connection between nodes and edges. 
Article/ citation link: A. Havolli, A. Maraj and L. Fetahu, “Building a content-based recommendation engine model using Adamic Adar Measure; A Netflix case study,” 2022 11th Mediterranean Conference on Embedded Computing (MECO), 2022, pp. 1-8, doi: 10.1109/MECO55406.2022.9797139.

How AI Integration Has Improve: 
According to this topic article, the reason Netflix’s services are so popular worldwide is that the company uses innovative technology like AI and machine learning to provide consumers with more appropriate and intuitive suggestions. This article explains how Netflix uses artificial intelligence, data science, and machine learning. Over twenty years Netflix still trying to improve it service with AI to provide customers with the greatest possible service and experience. The AI engine keeps an eye on the flow of info and sometimes takes over so that it may make judgments and suggestions at predetermined moments. And it also considers your viewing habits to provide Netflix recommendation. Quoted that “Users can take charge of their multimedia streaming and customize their interactions owing to the system’s ability to compile and recommend content based on their preferences.” 
Article/ Citation Link: Simplilearn. (2022, November 17). Netflix recommendations: How netflix uses AI, Data Science, and ML: Simplilearn. Simplilearn.com. Retrieved December 18, 2022, from https://www.simplilearn.com/how-netflix-uses-ai-data-science-and-ml-article#:~:text=The%20reason%20why%20Netflix’s%20services,more%20appropriate%20and%20intuitive%20suggestions.

Conclusion:
Netflix is a large streaming service; it gives users the ability to watch what they want and keep track of what the user is watching, to then recommend them to something as similar as what the user is already watching. And there is a lot more that Netflix do to keep their platform progressing, But the way Netflix algorithms work, would be interesting for somebody. Because it has an AI integration which is a machine learning, that technically help with Netflix recommendations by keeping tracks with what the users is watching and what they do not watch frequently, and then sometimes take over so that it makes judgments and suggestions at predetermined moments. And speaking of Netflix recommendations, the system of it required an engine model, by using “Academic Adar Measuring.” which to build the engine first on a graph they use an undirected graph to show the connection between nodes and edges. And finally, there is a Hybrid Recommendation Algorithm that I talk about, above this page. So, now my question is those other streaming platforms use Netflix’s algorithm tactics?