Business Studies

I have 9 articles already done and summarized. The page amount does not matter, there just needs to be about 16 total sources. The articles must be peer reviewed journals (or professional, reliable sources). The topic of the research paper is comparing and contrasting how streaming services such as Netflix and Hulu use data and algorithms to determine which shows to stream for consumers (How they pick which shows to display/stream) and to learn more about it. I have attached my prospectus to give a better idea of what kind of articles should be included, along with the start of the annotated bibliography. After each article, there should be a 130-150 (roughly) word summary of the article, clear but concise.

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Annotated Bibliography

Chen, L., Zu, S., & Zhou, H. (2018, September 1). Hulu video recommendation: From relevance to reasoning. Hulu video recommendation | Proceedings of the 12th ACM Conference on Recommender Systems. Retrieved November 5, 2021, from https://dl.acm.org/doi/abs/10.1145/3240323.3241730.

Summary:

This article covers the two different ways that Hulu uses data in order to determine what consumers want to watch. The store-shelf and autopay. The first requires a list of videos to maximize the chance that a viewer would pick one of them to watch. The second requires a sequence of video recommendations such that the viewer would continuously watch within the current session. These two technological advances are part of Hulu’s recommender system and help viewers to decide what they want to watch. Recommendation results are the targets, viewed as inputs for the reasoning task, consisting of pairs of relevant entities, i.e. a source node and a destination node in a knowledge graph. The recommendation reasoning task is to learn a path or a small directed acyclic subgraph, connecting the source node to the destination node.

Gomez-Uribe, C. A., & Hunt, N. (2015). The Netfix recommender system: Algorithms,

business value, and innovation. ACM Transactions on Management Information

Systems, 6(4), 13:1–13:19

Summary:

This article breaks down the recommender system that Netflix uses and explains how it became the service that it is. The article covers in depth the specific services that Netflix has in order to collect data and know what to show them to watch. From pools of research the goal of the recommender system is to not let consumers lose interest in a short amount of time, therefore it pools various shows together as recommendations as to what to watch next that is of similar content. The article also covers the handful of other data logs that Netflix offers such as their “Top Picks” and “Watch Next”. These are all ways of making the consumer feel like they have control and ways of collecting data to determine what the consumers want and like to watch.

Hadida, A. L., & Lampel, J. (2020, January 30). Hollywood studio filmmaking in the age of … – springer. Hollywood studio filmmaking in the age of Netflix: a tale of two institutional logics. Retrieved November 4, 2021, from https://link.springer.com/content/pdf/10.1007%2Fs10824-020-09379-z.pdf.

Summary:

This article serves the purpose of elaborating in great depth of the history of Hollywood studios and how the idea of what consumers wanted to watch transpired into today’s world. This article provides much needed information about where the ideas derived from in terms of to capture the shows and movies consumers like. The article covers details as to Netflix’s budget and how they use that towards distributing movies and shows consumers want to watch. Netflix’s intended yearly output of 55 movies per year, 35 have budgets of up to 20 million USD, with a majority (75%) consisting in genre movies designed to appeal to hyper-segmented target consumersFootnote14 whose profiles are primarily determined using advanced data analytics.Footnote15 Online streaming services occasionally cast movie stars in their higher budget films or shows—essentially to increase visibility and attract more subscribers.

Kainjuj, J. (n.d.). Hulu – Research Report. Retrieved November 4, 2021, from http://jessicastasie.com/wp-content/uploads/2016/05/KAINUJ_Final_IAKM60113.pdf.

Summary:

This article covers aspects of Hulu such as their areas of succeeding and failing with their streaming services. There is a section of this article which covers what aspects of Hulu customers are satisfied with, and which areas they are not satisfied with. The report breaks down that overall, suggested videos are an area in which Hulu suffers. This report displays that Hulu struggles with meeting the needs of the consumer because their recommended are missing the mark for consumers. Hulu falls to its competitors when it comes to recommending shows for their views, and consumers are noticing.

Maddodi, S., & K, K. P. (2020, February 21). Netflix bigdata analytics – the emergence of data driven recommendation. SSRN. Retrieved November 4, 2021, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3473148.

Summary:

This article covers the history of Netflix. It goes back to its roots and talks about how it has evolved with time. The main takeaways from this article is the section regarding the big data analytics and how Netflix was the first streaming service to use this idea in order to better their customer wants and needs. Netflix actually held a competition for who could design the best system for their service. The recommender system ended up being the winner. The article continues to break down different aspects of the system an all of its functions. This article will be beneficial to my research because it specifically talks about Netflix and how they use their big data. It also specifically breaks down what their system is and how it came to be.

McClinton , D. (2020, October 13). Is artificial intelligence controlling what you stream on Netflix, hulu? Thomasnet® – Product Sourcing and Supplier Discovery Platform – Find North American Manufacturers, Suppliers and Industrial Companies. Retrieved November 11, 2021, from https://www.thomasnet.com/insights/is-artificial-intelligence-controlling-what-you-stream-on-netflix-hulu/.

Summary:

This article describes in depth how Netflix uses algorithms to determine which shows to present to its consumers. It talks about the different ways, aside from the recommender system, Netflix also strategically uses data and algorithms to place certain shows up front for consumers to view. The article talks about the Personal Video Ranker (PVR) which ultimately uses data to find similar shows similar to those that consumers have clicked on. It then goes on to compare Hulu and Netflix and their different ways of collecting data in order to stream. While Netflix is more based on data and algorithms, Hulu focuses more on human interaction. The article then goes into depth as to how Hulu provides fast and relevant content to its viewers and how this is an advantage over its competitor, Netflix. The article then goes on to mention how not all feedback is good feedback and sometimes views are actually not happy with their recommended shows. Almost as if the algorithms made it harder.

Patterson, E. (2021, June 3). From network syndicator to Adult Disney: A brief history of Hulu Eleanor Patterson / auburn university. Flow. Retrieved November 5, 2021, from https://www.flowjournal.org/2021/06/brief-history-hulu/.

Summary:

This article will serve as the purpose of helping me to develop the history of Hulu and how their system came to be. This article covers the history of Hulu and what it’s original purpose was. Hulu was originally designed as a joint venture between NBC (Comcast) and New Corp. It was designed to be a technological device to hopefully stop users from watching illegal copies of uploaded clips on YouTube. The streaming service then transpired into being a service similar to Netflix. Hulu distinguished itself from these other streaming platforms by offering next-day streaming of current network programs for free to visitors, as well as offering subscribers a catalog of films and television series to view online. Only content professionally produced by legacy media companies was hosted on Hulu, and the platform attempted to balance the ad-free appeal of piracy, iTunes and early YouTube, with broadcasting’s legacy of selling the commodity audience to advertisers.

Wen, Z. (2008, December 12). Recommendation System Based on Collaborative Filtering. Zheng Wen – Publication. Retrieved November 11, 2021, from http://zheng-wen.com/publication.html.

Summary:

This article breaks down into great detail the actual algorithms Netflix uses to determine which shows to display for its consumers. The article breaks down the actual mathematical problems and formulas used to dissect viewer content. There are a few different ideas behind this. Similarities, newcomer predictions and item based algorithms are all ways that are broken down throughout this article that are used to predict view content. Using a combination of these tactics, streaming services, such as Netflix, are able to gather data and use it to make their consumers happy with what it popping up on their screen.

Ranjan, A. A., & Ra, A. (2019, April 7). An Approach for Netflix Recommendation System using Singular Value Decomposition. An approach for netflix recommendation system using singular value decomposition: Computer and Mathematical Sciences Journal : Computer and Math’s Journal : Computer and Mathematical Journal. Retrieved November 12, 2021, from http://www.compmath-journal.org/redirect/1063/Ankur-A-Ranjan-Amod-Rai-Saiful-Haque-Bhanu-P-Lohani4and-Pradeep-K-Kushwaha5/An-Approach-for-Netflix-Recommendation-System-usingSingular-Value-Decomposition.html.

Summary:

This article focuses on the content filtering approached used by Netflix’s recommender system. The article talks about the neighboring method, which entails the comparisons of items that are marked similarly. The Latent Factor Model analyzes the handler as well as the item interaction or communication with the medium. The article then goes on to discuss the specific algorithms and methods used.

Pajkovic, N. (2021). Algorithms and taste-making: Exposing the Netflix Recommender System’s operational logics. Convergence, 13548565211014464.

Summary:

The article has focused on how recommendation systems such as Netflix Recommender systems work. To respond to the question on how the recommender system works, the article has examined how the algorithms are influencing the processes of taste making as well as re-evaluated some vital theoretical perceptions that have come to direct the state of the algorithmic culture. Netflix recommender system is driven by machine learning through a combination of collaborative filtering and content based filtering algorithms used to recommend content. Content based filter utilizes the users’ previous information which are obtained from their interfaces. Collaborative filtering relies on similar data extraction method but develops recommendations on the basis of weighted combination of users’ preferences. Netflix recommender system also relies on A/B testing to examine the efficiency of different recommendation variants and algorithm through the evaluation of experimental and control groups of Netflix viewers.

Frey, M. (2021). Netflix Recommends: Algorithms, Film Choice, and the History of Taste. Univ of California Press.

Summary:

The article describes how the algorithmic recommender systems that are deployed media companies are used to suggest content based on the user viewing histories. They are used to inspire curated and personalized media. The article has scrutinized one of the world’s most subscribed and common streaming service, Netflix. Netflix recommender system is used to show the constellations of sources that show the real viewers use to select series and films in the digital age. The article shows that film choice and the history of taste have been influenced by the algorithms which are used to suggest content.

Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A netflix case study. In Recommender systems handbook (pp. 385-419). Springer, Boston, MA.

Summary:

The article focuses on the use of recommender systems through analyzing a case study of Netflix. The evolution of the industry into application of recommender systems has been powered by accessibility of user information and the level of notice. The article has highlighted some of the main lessons from Netflix recommender system, describing several techniques and approaches that are used in the real world recommendation system. The Netflix Prize has put a interest on the use as well as significance of recommender systems. This article has offered an up-to-date impression of the recommender system methods that are applied in the industrial situation.

Li, H., Cui, J., Shen, B., & Ma, J. (2016). An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing, 210, 164-173.

Summary:

The article analyzes how online media sharing sites such as Hulu have introduced new challenges in program recommendation in online platforms. Intelligent program recommendation algorithms have been used on sites such as Hulu for a series of social computing models and data mining approaches. Recommendation systems have been applied in online media streaming platforms to facilitate intelligent movie recommendations that can be made through data mining and mining microblogs. The recommendation systems have been used frequently on movies due to their simplicity in accessing test data. Data expressed in microblogs mines user preferences which has built an effort to bridge the gap between TV and movie viewers realm with social network activities.

Hong, S. E., & Kim, H. J. (2016, July). A comparative study of video recommender systems in big data era. In 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 125-127). IEEE.

Summary:

The article focuses on a comparative study of the video recommender systems in big data era. Recently, there has been a rise in the spread of high bandwidth internet access and abundant of content generation especially video contents. The incoming bid data era has led to video content creators to develop suitable recommender systems. The article has recommended for recommendation technologies for famous companies: Hulu, You tube, Netflix and Amazon to gain an understanding of the basic differences between recommendation algorithms and examine the cons and pros of the different recommender systems. Most recommender algorithm systems have relied on collaborative filtering. Meta data has also played an essential role in the recommender algorithms.

Jones, M. T. (2013). Recommender systems, Part 1: Introduction to approaches and algorithms. IBM DeveloperWorks, 12.

Summary:

The article has two parts that are used to explain the recommendation systems. The first part is a two part series that explains the ideas linked to the recommendation systems and introduces how the algorithms are used to sorting through massive data amounts from potential users. The second part offers an understanding about some open recommendation systems that can be used in large scale commercial and social websites. Some of the recommended systems include Netflix, Amazon, Hulu and Linked In. The article has also offered some basic approaches: content based or collaborative filtering and hybrid approaches. The article has also indicates some challenges with recommender systems such as scalability and privacy protection considerations.

Raju, S., & Poravi, G. (2018, April). Recommender System for Generic User Preferences for Online Content. In 2018 3rd International Conference for Convergence in Technology (I2CT) (pp. 1-4). IEEE.

Summary:

The article offers a detailed analysis of methods and techniques that are utilized by large companies such as Hulu, Netflix, You Tube and Amazon to handle the speed at which content is being uploaded on the internet within very short periods of time. According to Raju and Poravi (2018), vast amount of content on the internet has made it difficult for the users to examine the best content that they desire. High end content providers have therefore decided to rely on their custom recommendation systems to allow the users to better access their content.

References

Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A netflix case study. In Recommender systems handbook (pp. 385-419). Springer, Boston, MA.

Frey, M. (2021). Netflix Recommends: Algorithms, Film Choice, and the History of Taste. Univ of California Press.

Gomez-Uribe, C. A., & Hunt, N. (2015). The Netfix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information

Jones, M. T. (2013). Recommender systems, Part 1: Introduction to approaches and algorithms. IBM DeveloperWorks, 12.

Kainjuj, J. (n.d.). Hulu – Research Report. Retrieved November 4, 2021, from http://jessicastasie.com/wp-content/uploads/2016/05/KAINUJ_Final_IAKM60113.pdf.

Li, H., Cui, J., Shen, B., & Ma, J. (2016). An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing, 210, 164-173.

Pajkovic, N. (2021). Algorithms and taste-making: Exposing the Netflix Recommender System’s operational logics. Convergence, 13548565211014464.

Raju, S., & Poravi, G. (2018, April). Recommender System for Generic User Preferences for Online Content. In 2018 3rd International Conference for Convergence in Technology (I2CT) (pp. 1-4). IEEE.

Wen, Z. (2008, December 12). Recommendation System Based on Collaborative Filtering. Zheng Wen – Publication. Retrieved November 11, 2021, from http://zheng-wen.com/publication.html.

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