Difference between revisions of "Netflix Algorithm"

Revision as of 03:47, 8 February 2023

Netflix, Inc. is an American subscription video-on-demand over-the-top streaming service and production company based in Los Gatos, California. Netflix offers personalized recommendations, to help users find shows and movies of interest to them, which are at the core of the product. To this end, Netflix develops and uses a recommendation system using algorithms. Netflix lets users choose a few titles that they like when they first make a profile^[1].

Based on this, first form a recommendation, and develop the algorithm by tracking the following while you are using the app. Below, the ethics of Netflix's algorithm are analyzed according to the criteria established by each of the three institutions: Common Microsoft, Sense Media, and Federal Trade Commission.

Netflix Algorithm Recommendation Engines

Netflix’s recommendation has the most successful engine. According to News 10, 47% of North Americans use Netflix over other video streaming services. It’s so accurate that 80% of Netflix viewer activity is driven by personalized recommendations from the engine^[2]. As of 2016, Netflix's recommended engine saves more than $1 billion a year in customer acquisition costs for Netflix. Netflix's algorithm ranks and constructs titles using machine learning systems such as reinforcement learning, matrix factorization, and other algorithmic approaches.

How Netflix started using algorithms

In 1998, as a DVD mail rental company, Netflix accumulated data on people, and the fact that the data was unique in the world made Netflix the best streaming service^[3]. These data are big data that includes people's information, gender, region, age, viewing time, and taste. Using the customer database to open all of House of Cards in one day - based on the culture of watching series with family on weekends and holidays, marketing to the taste of U.S. citizens.

How Netflix’s algorithms work

This is one of the most successful algorithms for all businesses. Netflix Recommendation Engine (NRE) is made up of algorithms that filter content based on each individual user profile. The engine helps users to find a show or movie to enjoy with minimal effort and spend more time on it.

User starts off creating their profile by selecting a few titles that you like. If users do not want to do it, diverse and popular sets of titles are represented for you.

Netflix Algorithm Engine observes the following ^[4].and accumulates data. - User’s interactions with its service; viewing history and rating other titles

- Other members with similar tastes and preferences on our service

- Preference and frequency about the titles such as genre, categories, actors, year of release, etc.

- The time-of-day user watches

- How long user watch, when the user paused, rewound, or fast-forwarded

- What scenes users have viewed repeatedly

- The device used to stream

- Demographic information, such as age or gender, is not included when making decisions

The Algorithm Engine is enhanced again overweighing the titles that users watched more recently. Therefore, NRE represents a ranking of titles in a way that is designed to present the best possible ordering of titles that you may enjoy. The most strongly recommended titles start on the left of each row and go right. This algorithm functions when users try to make searching. The top results will also be personalized^[5].

Ethics in Algorithms: Using artificial intelligence and algorithms

The use of Algorithms – to make predictions, recommendations, or decisions has enormous potential to improve welfare and productivity. However, it also implies risks such as unfair or discriminatory outcomes or perpetuating existing socioeconomic gaps.

Actually nearly early every week, a new report of algorithmic misbehaviors emerges. Federal Trade Commission of the United States government offers important lessons about how companies can manage the consumer protection risks of its algorithms and Artificial intelligence^[6].

Companies or any organizations that use algorithms or benefit from algorithms should inform customers transparently how the automated tool is used. Customers should also be notified of the adverse action caused by its use. The company is also encouraged to notify customers whenever they make changes in decisions. In order to do that companies must know what data is used in their model and how that data is used to arrive at a decision. Next, you need to ensure that your decisions are fair by giving consumers access and an opportunity to correct information used to make decisions about them and by ensuring that your data and model are robust and empirically sound. Lastly, hold yourself accountable for compliance, ethics, fairness, and non-discrimination^[7].

Privacy

Netflix Terms of Use

They gather personal data such as names, email addresses, postal codes, payment information, phone numbers, and other data that is automatically gathered when users access the Service. The Netflix terms further say that they share or disclose personal data with third parties (referred to as "Service Providers") to carry out tasks on their behalf or aid in the delivery of services. Additionally, the agreements say that Netflix does sell users' private information to third parties for financial gain.

Netflix uses personal data to distribute and customize marketing or advertising, as well as to track user responses to emails, marketing campaigns, and online advertisements on third-party websites. Additionally, according to Netflix's conditions, they take reasonable precautions to ensure the security and confidentiality of the personal data they acquire from Service users.

According to Common Sense Media^[8], an organization that reviews and provides ratings for media and technology with the goal of providing information on their suitability for children. They evaluated every each privacy rating. A higer rating (up to 100%) indicates that the product has more open privacy policies and better methods for safeguarding user data. The score is most used as a measure of the amount of further research someone will need to conduct in order to make an informed choice about a product. Netflix scored 50% and some concerns about privacy evaluation are as follows.

Responsible Use ^[9]

It is not known whether Netflix continuously monitors (viral) social interactions occurring in the real world and reflects them in uploaded works. In terms of reporting content, although the algorithm can distinguish, this does not filter or block content that is deemed inappropriate for the user.

Personal Data Gathering

In order to analyze personalized recommendations, users are frequently required to provide personal information, which puts them at risk for privacy violations ^[10].

Data Sharing: Protecting data from third parties ^[11]

Collected data is shared with third parties and Netflix indicates the purpose for sharing personal information with them. However, it is not clear for what purpose it is shared for research, product improvement, data analytics, and/or third-party marketing. When it comes to third-party data access, they are authorized to access a user's personal information.

Data Security: Protecting against unauthorized access ^[12]

Users are required to create an account by authenticating additional personal information for the first time. However, this information is also unknown whether third-party is given access or protected by the company. Also, it is unclear what jurisdiction a user's personal information is subject to.

Race stereotypes & inequalities

As part of the general shift toward multiculturalism, mass marketing that caters to everyone has made way for ethnically particular niches that profit from calls for variety. Using tailored movie posters featuring Black supporting cast members, Netflix can persuade Black viewers to click on a suggestion they might not have otherwise. Importantly, Netflix, which relies on targeted marketing, does not need to ask customers about their race because they use prior viewing and search histories as proxies to estimate who will be drawn to certain cast movie posters ^[13].

References

1. ↑ How Netflix's Recommendations System Works. Help Center. (n.d.). Retrieved January 25, 2023, from https://help.netflix.com/en/node/100639#:~:text=We%20estimate%20the%20likelihood%20that,preferences%20on%20our%20service%2C%20and.
2. ↑ Meltzer, R. (2020, July 7). How Netflix utilizes Data Science. Lighthouse Labs. Retrieved January 25, 2023, from https://www.lighthouselabs.ca/en/blog/how-netflix-uses-data-to-optimize-their-product.
3. ↑ Plummer, L. (2017, August 22). This is how Netflix's top-secret recommendation system works. WIRED UK. Retrieved January 25, 2023, from https://www.wired.co.uk/article/how-do-netflixs-algorithms-work-machine-learning-helps-to-predict-what-viewers-will-like.
4. ↑ How Netflix's Recommendations System Works. Help Center. (n.d.). Retrieved January 25, 2023, from https://help.netflix.com/en/node/100639#:~:text=We%20estimate%20the%20likelihood%20that,preferences%20on%20our%20service%2C%20and.
5. ↑ Meltzer, R. (2020, July 7). How Netflix utilizes Data Science. Lighthouse Labs. Retrieved January 25, 2023, from https://www.lighthouselabs.ca/en/blog/how-netflix-uses-data-to-optimize-their-product.
6. ↑ Staff, the P. N. O., Staff, D. P. I. P. and C. T. O., Smith, A., & Fair, L. (2022, June 13). Using artificial intelligence and algorithms. Federal Trade Commission. Retrieved January 25, 2023, from https://www.ftc.gov/business-guidance/blog/2020/04/using-artificial-intelligence-and-algorithms.
7. ↑ Staff, the P. N. O., Staff, D. P. I. P. and C. T. O., Smith, A., & Fair, L. (2022, June 13). Using artificial intelligence and algorithms. Federal Trade Commission. Retrieved January 25, 2023, from https://www.ftc.gov/business-guidance/blog/2020/04/using-artificial-intelligence-and-algorithms.
8. ↑ Common sense privacy evaluation for Netflix. The Common Sense Privacy Program. (n.d.). Retrieved January 26, 2023, from https://privacy.commonsense.org/evaluation/Netflix
9. ↑ Common sense privacy evaluation for Netflix. The Common Sense Privacy Program. (n.d.). Retrieved January 26, 2023, from https://privacy.commonsense.org/evaluation/Netflix
10. ↑ DeLeon, H. (2019, April 24). The ethical and privacy issues of recommendation engines on media platforms. Medium. Retrieved January 25, 2023, from https://towardsdatascience.com/the-ethical-and-privacy-issues-of-recommendation-engines-on-media-platforms-9bea7bcb0abc
11. ↑ Common sense privacy evaluation for Netflix. The Common Sense Privacy Program. (n.d.). Retrieved January 26, 2023, from https://privacy.commonsense.org/evaluation/Netflix
12. ↑ Common sense privacy evaluation for Netflix. The Common Sense Privacy Program. (n.d.). Retrieved January 26, 2023, from https://privacy.commonsense.org/evaluation/Netflix
13. ↑ Benjamin, R. (2020). Race after technology: Abolitionist Tools for the new jim code. Polity. 10. Common sense privacy evaluation for Netflix. The Common Sense Privacy Program. (n.d.). Retrieved January 26, 2023, from https://privacy.commonsense.org/evaluation/Netflix