The Netflix Recommender System: Algorithms and Cultural Impact

This report examines the Netflix Recommender System, analyzing two academic journals to understand its algorithms, business value, and cultural impact. The report explores the system's evolution, including the Netflix Prize competition and the challenges of A/B testing. It delves into the business motivations behind the system, its role in member engagement, and the innovation in language-aware and presentation bias. The report also discusses the influence of the Netflix Prize on algorithmic culture, highlighting the shift from collaborative filtering to latent element assimilation. The conclusion emphasizes the importance of understanding the intersection of computation and culture in the current era, where algorithms significantly influence how we experience and interact with media.

Running head: THE NETFLIX RECOMMENDER SYSTEM
The Netflix Recommender System
Name of the student:
Name of the university:
Author Note

1THE NETFLIX RECOMMENDER SYSTEM
1. Abstract
The recommendation systems change the approaches by which the inanimate websites are found to
be communicating with the users. The survey paper is created to demonstrate the two academic
journals keeping the case of Netflix Recommendation Algorithm in mind.
2. Keywords:
Netflix Recommendation Algorithm, recommender system, algorithm, culture.

2THE NETFLIX RECOMMENDER SYSTEM
Table of Contents
3. Introduction:......................................................................................................................................3
4. Related work, description of recommendation algorithm:................................................................3
4.1. The Netflix Recommender System: Algorithms, Business Value and Innovation:...................3
4.2. Recommended for you: The Netflix Prize and the production of algorithmic culture:..............5
5. Conclusion:........................................................................................................................................8
6. References:........................................................................................................................................9

3THE NETFLIX RECOMMENDER SYSTEM
3. Introduction:
The recommendation systems have been changing the methods in which the inanimate
websites have been communicating with the users. They identify the recommendations
autonomously for every user on the previous searches and purchases along with the behavior of
other users.
The rating prediction algorithm researched in the Netflix Prize is referred to as the “Netflix
Recommendation Algorithm”. The following survey paper is developed to analyze the two academic
journals referring to the case of Netflix Recommendation Algorithm.
4. Related work, description of recommendation algorithm:
4.1. The Netflix Recommender System: Algorithms, Business Value and Innovation:
The above article has analyzed various algorithms making up the Netflix recommender
system along with the business purposes. It has also included the roles of search and the relevant
algorithms turning to be recommendation problems also. The motivations and the review of the
approach are also explained for improving the recommendation algorithm [1]. It has combined the
A/B testing concentrating on the developing member retention and the mid-term engagement.
Further analysis is done on the offline experimentation through the engagement data of the historical
members. Lastly, the issues to design and interpret the A/B tests are discussed long with the present
sectors of the focused innovation. This has been including the recommender system aware of the
language and areas.
The Netflix has been lying in the mid of Internet and storytelling. The enterprise has been
permitting the members to steam videos in their various collections of TV shows and movies at any

4THE NETFLIX RECOMMENDER SYSTEM
time over the broad range of the devices connected to the Internet. The Netflix Recommender
System has included the Internet TV about the choice. This includes what to watch, when and where.
This is compared with the straight cable and broadcast systems offering whatever are playing on the
favorite channels [8]. The advantages of Internet TV have been that that can carry videos from the
broader catalogue that has been appealing to the full range of tastes and demographics.
The Netflix Recommendation problem is the equipment to the challenges to predict some
stars that the person was rating the video after watching. Regarding the business values, the
enterprise has been seeking to grow the business on an extensive scale. This is turning to the
distributor and producer of shows and the movies with the total global reach. They have developed
and used the recommender system since they trusted that this had been central to their business for
various number reasons. The system has been helping the win moments of truth [2]. As any member
has been starting any session and helping the members to seek anything engaging with the few-
seconds. This has been preventing the abandonment of the service for the options of alternative
entertainment.
The algorithms are improved using their individual, collective intuition for choosing the best
variant of the recommendation algorithm fielding the wrong answer. This has been impossible
frequently especially while trying to state the good from the vast recommendations apart. The results
of the A/B testing have been the most significant source of data to make the decisions of the
products [15]. At maximum cases, the tests have been extremely informative. However, the
sophistication of statistics going to their analysis and design interpreting their A/B tests has been
stating apart partly.
Further, the article has described primary open challenges. The company has been examining
to develop the recommender system for the past decades. However, they have been trusted that the

5THE NETFLIX RECOMMENDER SYSTEM
recommendations could be better that are estimated to be on today [2]. The primary open challenges
have been revolving around the A/B testing and the others across the recommendation algorithms.
Firstly a better alternative to the offline experimentation has been allowing iteration quickly.
However, this has been more predictive of the A/B test results. Secondly as per as the global
algorithms are concerned, the models must be considered taking how the languages have been
available for the audio and the subtitles. This is for every video matching the styles throughout the
world. Next the controlling of the presentation bias is considered. Here the statistical models and the
standard mathematical techniques are used to create the recommendations have not found the
feedback loop. Moreover, the page construction has been new and unexplored [3]. Lastly, there have
been the issues regarding the account sharing. It is seen that a considerable percentage of the profiles
has been used by various people in the household. Lastly, the report has discussed different useful
for supporting every recommendation. There have been multiple synopses, images and the other
evidence used to present every proposal. It is opted to highlight various aspects of the video like the
director, actor involved in that and any more [2]. The sector of evidence has engaged the most useful
evidence for presenting every recommendation.
Thus the article has shown that the regional approach of Netflix is used has been a solution of
stop-gap for preventing the differences of local catalog from throwing away the recommendation
algorithm. The organization was very much worried initially in running their algorithms that has
been estimated to be working well. This occurs as they pulled the data from one individual country
and over a single catalog. Further in that case they tried around places where the catalog has been
differing and the recommendations were very poor.
However the use if the recommender system of Netflix has been impressive and the product
were very much advanced. They were combining all the components successfully together to

6THE NETFLIX RECOMMENDER SYSTEM
develop a product that must be successful commercially. As the recommender system is built, it has
been worth looking at what the company’ team has been doing to achieve any inspiration.
4.2. Recommended for you: The Netflix Prize and the production of algorithmic culture:
The article has analyzed the way in which the algorithmic data processing has been
impacting the significance of the term culture and more precisely the cultural practice. The questions
have been addressed through focusing on the Netflix Prize of the current movie recommendation
system slightly. Though billed as the technical issue for the engineers, the article has debated that the
Netflix Prize was the effort for interpreting the term culture again. The report has explored the
semantic and conceptual work needed to render the processing systems of algorithmic information
legible in the forms of cultural decision makings [4]. It has also been denoting the effort to add the
dimension and depth to the idea of the algorithmic culture. The competition has been offering a
distinct scope for scrutinizing the details of the systems that peers have been remaining mainly secret
from the view [9]. Moreover, to the hunt of connecting the people to the movies they like the Netflix
Prize has interconnected the algorithms to the art. Through doing this, the conceptual foundations of
the culture are intervened.
The Netflix Prize has attracted the specialists of machine learning experts from the computer
science. This is to a lesser extent. A small improvement of about ten percent is pitched the
competition in the market as the technical and quantitative challenge. This took about three years to
finish. It included some contestants claiming to be working about ten to twenty hours on the
algorithms. It has been underscoring the quantity of the Endeavour [10]. However, the participants
also found the degree to the extent in which the technical challenges have been going hand-in-glove
with the interpretive one. The finding of the importance of the customer ratings of Netflix and the

7THE NETFLIX RECOMMENDER SYSTEM
broader problem regarding the way to adjudicate the cultural values delivered the vexing as the
engineering.
The Netflix Prize has incentivized the researched regarding the television shows and movies.
They have also included the people, proposed new models of the cultural identity that is latent in the
dataset and has been presumably the social [11]. For doing that the contestants tended to reject the
dominant demographic categorizations of emerging the systems of identification. The work of the
team Pragmatic Theory has been indicating the argument that the broader classifications have failed
to capture the subtle factors related to the decisions that are made about the cultural goods consumed
by them. It is revealed that the movie or the user data has not been helpful. This is because the
various algorithms have been too kind to capture the nuances and details that have been influencing
the ratings of the users.
Regarding the aesthetics, ideas and objects, the findings of Netflix Prize has shifted their
approach of dominance to the recommendation systems. This is done from the more conventional
collaborative filtering to the assimilation of the latent elements that are predictive [5]. However, the
essential corporative investment in this contest has turned out that the enterprise has never wholly
operationalized the successful algorithm. The decision has been driven partially by the expenses
related to using the onerous algorithm for the data processing commercially. However, most
importantly it has seemed connected to the fact that about the halfway by the competition, the
Netflix got changed [12].
After its birth, Netflix spent the initial decades by interfacing with the clients mainly by the
websites. Here the customers generated the series of videos that they wanted to see. At first, the
streaming service has been accessible through the browser-based web portal [6]. However, within
one year, both the Roku set-top streaming device and Xbox game console boasted the capability to

8THE NETFLIX RECOMMENDER SYSTEM
stream the videos of Netflix. As the service-on-demand took off, the other famous developers started
including that also. The service then reached the Apple ecosystem in the format of the iOS app. This
also added the feature on the Apple TV less than one year after the ending of the Netflix Prize [14].
The Android App landed one year after joining the host of the other Wi-Fi connected devices
of the Netflix streaming enabled. As Netflix needed to dispense with DVDs, the move towards
streaming was primarily changed how that conceived the core business [13].
Lastly, the Netflix Prize has also been bringing some queries. It started to determine what
occurs as the engineers or the algorithms turn out to be the significant arbiters of the culture similar
to literary critics, films and arts. They also needed to find out the way in which they have been
contesting the discrimination and identification forms that are computationally intensive operating in
the in-depth background of the life of the people. This also includes the formats that underpinning
mathematical principles exceeded the reasonable degree of the technological competency. There
have been further queries about what has been at stake to optimize the cultural artifacts for assuring
the more favorable reception by human audiences and algorithms. These questions are opened up by
the Netflix Prize, and it has been hardly settling them. This has been nonetheless offering the
required view-point on what the culture has been meant.
5. Conclusion:
It has been evident from the articles that the culture has been what it has been once known to
be. This has been all the more reason to understand it anew. Devoid of this, people have been risking
the hampering of the ability to take part meaningfully in the world where computation and culture
have been turning to be less distinguishable from each other. In the current era, the humanity is
lucky enough to be witnessing the changes done by the Internet. Similar to the prior significant

9THE NETFLIX RECOMMENDER SYSTEM
technological innovations, the Internet has been imparting a profound effect on storytelling. Here
lies the position of Netflix at the conjunction of storytelling and Internet.

10THE NETFLIX RECOMMENDER SYSTEM
6. References:
[1]"1.3 Different types of manager", Soas.ac.uk, 2017. [Online]. Available:
https://www.soas.ac.uk/cedep-demos/000_P531_MRD_K3736-Demo/unit1/page_11.htm.
[Accessed: 08- Nov- 2017].
[2]K. Rosen, "The Seven Types of Managers—Where Do You Stand? |
AllBusiness.com", AllBusiness.com, 2017. [Online]. Available: https://www.allbusiness.com/the-
seven-types-of-managers-where-do-you-stand-10207093-1.html. [Accessed: 08- Nov- 2017].
[3]"Types of Management | Boundless Business", Courses.lumenlearning.com, 2017. [Online].
Available: https://courses.lumenlearning.com/boundless-business/chapter/types-of-management/.
[Accessed: 08- Nov- 2017].
[4]"The six different types of managers and how to work with them | Latpro.com", Latpro.com,
2017. [Online]. Available: https://www.latpro.com/c/the-six-different-types-of-managers/.
[Accessed: 08- Nov- 2017].
[5]"Strategic Management :: Types Of Managers", Introduction-to-management.24xls.com, 2017.
[Online]. Available: http://www.introduction-to-management.24xls.com/en114. [Accessed: 08- Nov-
2017].
[6]"The 4 Kinds of Managers You'll Encounter as an Intern (And How to Handle
Them)", Themuse.com, 2017. [Online]. Available: https://www.themuse.com/advice/the-4-kinds-of-
managers-youll-encounter-as-an-intern-and-how-to-handle-them. [Accessed: 10- Nov- 2017].
[7]"The 4 Types of Project Manager", Harvard Business Review, 2017. [Online]. Available:
https://hbr.org/2017/07/the-4-types-of-project-manager. [Accessed: 10- Nov- 2017].

11THE NETFLIX RECOMMENDER SYSTEM
[8]"The Netflix Recommender System: Algorithms, Business Value, and Innovation - Semantic
Scholar", Semanticscholar.org, 2017. [Online]. Available:
https://www.semanticscholar.org/paper/The-Netflix-Recommender-System-Algorithms-Business-
Gomez-Uribe-Hunt/e9dd899f0e599eafb4fe47696c83d07d971c0088. [Accessed: 11- Nov- 2017].
[9]"Recommended for you: The Netflix Prize and the production of algorithmic cultureNew Media
& Society - Blake Hallinan, Ted Striphas, 2016", Journals.sagepub.com, 2017. [Online].
Available: http://journals.sagepub.com/doi/abs/10.1177/1461444814538646. [Accessed: 11- Nov-
2017].
[10]"Collaborative Filtering", Recommender Systems, 2017. [Online]. Available:
http://recommender-systems.org/collaborative-filtering/. [Accessed: 12- Nov- 2017].
[11]"Recommender Systems", Recommender Systems, 2017. [Online]. Available:
http://recommender-systems.org/. [Accessed: 12- Nov- 2017].
[12]"Manning | Practical Recommender Systems", Manning.com, 2017. [Online]. Available:
https://www.manning.com/books/practical-recommender-systems. [Accessed: 12- Nov- 2017].
[13]"Netflix United Kingdom - Watch TV Programmes Online, Watch Films Online", Netflix.com,
2017. [Online]. Available: https://www.netflix.com/in/. [Accessed: 12- Nov- 2017].
[14]"Roku", Roku, 2017. [Online]. Available: https://channelstore.roku.com/details/12/netflix.
[Accessed: 12- Nov- 2017].
[15]"Recommendation Engines: How Amazon and Netflix Are Winning the Personalization
Battle", Martechadvisor.com, 2017. [Online]. Available:
https://www.martechadvisor.com/articles/customer-experience/recommendation-engines-how-
amazon-and-netflix-are-winning-the-personalization-battle/. [Accessed: 12- Nov- 2017].