We would like to start by defing what Recommnder Yystem means . A recommender system or a recommendation system (sometimes replacing “system” with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.
In this section we will be analyzing the recommender sytem provided by Netflix .The Netflix business is a subscription service model that offers personalized recommendations, to help subscribers find shows and movies of interest. For this purpose they have created a proprietary, complex recommendations system.In most cases, these recommendations receives the highest hit which are then personalized to fit your preferences. They are driven by learning algorithms which is why each user’s recommendations are different. Having a better set of recommendations makes subscribers to easly browse through the kind of content they like to watch.
Whenever a subscriber accesses the Netflix service, their recommendations system strives to help find a show or movie to enjoy with minimal effort. The system estimates the likelihood that the subscriber will watch a particular title in their catalog based on a number of factors including: the subscribers interactions with theor service (such as viewing history and how the customer rated other titles),other members with similar tastes and preferences on ther service , and information about the titles, such as their genre, categories, actors, release year, etc.
In addition to knowing what you the subscriber have watched on Netflix, to best personalize the recommendations they also look at things like: the time of day the subscriber watches, the devices the subscriber is watching Netflix on and the duration as well .
Netflix is an internet streaming software which allows you to watch content through any internet connected device which include smart TVs, smartphones, tablets, game consoles etc. Netflix has 139 million subscribers globally. Target customers would be:
To get as much subscribers by improving the customer experience of their entertainment services. They try to get as much people to watch their content with unlimited hours.
As this assignment is based on Recommender systems, providing insights on how to improve their recommendations as this is the best way to better understand their customers indirectly.
Netflix recommender system is based on collaborative filtering therefore they need a certain amount of data to understand it before making recommendations. When users sign up, the software usaually require the individual to choose a few shows to jump start their recommendations. They learn about the customer’s preference based on:
Also, with their ratings system, every time subscriber leaves a rating, they are learning what the subscriber enjoys.
Netflix recommendations are heavily skewed towards what you are currently watching hence you get stuck in a recommendation rut or lack variety of choices. However, the more you watch, the more your suggested content becomes relevant.
Recommendations get diluted when multiple people are using the same profiles.
To show your approval, it is either a “thumbs up” or “thumbs down”.
Overall, we are satisfied with Netflix’s recommendation system .Could there be another way to increase engagement, besides design tricks, algorithms, and inter-episode ads? We believe Netflix should put its users back behind the wheel.
Here are a few ideas:
Due to the current trend on the anyone re-watching every movie or show that they previously watched is very slim will affect the efficiency of the sytem as it will lead the user’s browsing experience less optimal. As a solution for this we recommend removing it or moving the list to the bottom would be good.
Make it easy for people to share titles right in the platform or on social media. Offer a smarter way to dive in and explore content on your own.
A good search engine with listed results would be ideal,with filters like “Added date,” “Minimum user rating,” “Director,” or “Actor/Actress.”