Introduction:

The recommender system that I would like to mention is Netflix. Netflix offers personalized recommendations for their subscribed members by providing a streaming service to find shows or movies that would be of interest based on program viewings.Netflix is always testing and scheming. Each time you click play, pause, or - heaven forfend - stop watching TV altogether, it gathering data on your preferences. Spread across more than 300 million user profiles, this is a colossal amount of information. And it all feeds back into what you see when you next look for something to watch

Scenario Design Analysis:

1.Who are the target users?

The target users are the subscribed Netflix members. Netflix offers both domestic and global viewings.

2. What are their key goals?

Netflix goals at a high level is defined in its mission statement that states the following: “We promise our customers stellar service, our suppliers a valuable partner, our investors the prospects of sustained profitable growth, and our employees the allure of huge impact.” In addition, Netflix aims to grow and become the largest streaming subscription business both domestically and globally.

The subscribed Netflix member wants Netflix to provide the best service with not only undisrupted streaming but also a clear understanding of their habits that can change from time to time.

3. How can you help them accomplish those goals?

Netflix can help the members accomplish their goals by continuously keeping up with listening to their customers for new kinds of trends. In addition, it must continue to be current with the changing technology so that the streaming service will work with more internet- connected devices.

Reverse Engineer:

Netflix uses the personalized method where movies are suggested to the users who are most likely to enjoy them based on a metric like major actors or genre. Machine learning is necessary for this method because it uses user data to make informed suggestions. This way Netflix methodology accounts for the diversity in its audiences and its very large catalog.

Netflix uses the following data inputs to process their algorithms for the recommendations system:

. The members interactions with the Netflix service (ex. viewing history)

. Other members with similar preferences

. Information about the titles (ex. genre, categories, etc.)

. The time of day for viewings

. The devices the members are using for watching Netflix

. How long the members are watching

In addition, a member is initially asked to choose titles of movies/shows that they would be interested in. After that, any new titles that the member watches will supersede any of the initial preferences. In addition, Netflix ranks the preference titles.

Specific Recommendations:

A specific recommendation for Netflix is to capture not only the members current watching trends but what they still might be interested in viewing (do not supersede the old data) to avoid for the member to search an entire catalog. This could possibly even improve the accuracy of predictions.

References: https://help.netflix.com/en/node/100639 https://www.business2community.com/marketing/look-new-target-audience-netflix-subscribers-01813457