Recommendation algorithms are at the core of the OTT platforms. They provide users with personalised suggestions to reduce the amount of time/frustration to find something great content to watch.If you are curious to know how any recommendation works at platforms like amazon, netflix or spotify, this session is for you.
Recommendation algorithms are at the core of the OTT platforms. They provide users with personalized suggestions to reduce the amount of time/frustration to find something great content to watch. Because of the importance of such recommendations, OTT platforms like Netflix continually seek to improve them by advancing the state-of-the-art in the field.
With over 7,000 movies and shows in the Netflix catalog, it is nearly impossible for users to find movies they’ll like on their own. The large platform needs a recommendation system machine learning algorithm to automate the search process for users.
With over 139 million paid subscribers across 190 countries, 15,400 titles across its regional libraries— Netflix is the world’s leading Internet television network and the most-valued largest streaming service in the world.
Now you must be wondering how Netflix uses the recommendation system to personalize your suggestions. Netflix recommendation system has been implemented using data processing and natural language processing with python. Netflix’s recommendation system algorithm produce $1 billion a year in value in terms of customer retention. The major portion of Netflix users considers recommender systems quite personal. With 80% of Netflix views coming from their movie recommendation system. Netflix has set up 1300 recommendation clusters based on users viewing preferences.
Now let’s deep dive into each of the core terminologies used in the session.
Recommender systems aim to predict users’ interests and recommend products that quite likely are interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales. Data required for recommender systems comes from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves. In this session, we’ll focus on recommendation system using python.
There are two types of Recommendation system: Collaborative based and Content-based Recommendation system.
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do. Natural language processing in ai is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more.
We have dedicated libraries in python like python pandas which help us to do the task easily. In this session, we have used deep learning for natural language processing.
Data Processing is nothing but simply the manipulation of data by computer. It involves converting the raw data into a machine-readable format. It is usually performed in a step-by-step process by a team of data scientists and data engineers in an organization. The raw data is collected, filtered, sorted, processed, analyzed, stored, and then presented in a readable format.
The data processing cycle consists of a series of steps where raw data (input) is fed into a process (CPU) to produce actionable insights (output). Each step is taken in a specific order, but the entire process is repeated in a cyclic manner. The first data processing cycle's output can be stored and fed as the input for the next cycle.
There are different types of data processing based on the source of data and the steps taken by the processing unit to generate an output.
What are you waiting for? Learn to build your own recommendation system.
See you in the session.
Netflix was first founded in August of 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. The company began out in Scotts Valley, California, and has grown to become one of the world's leading internet entertainment platforms.
For millions, Netflix is the de-facto place to go for movie and TV streaming. According to sites like fortune.com, its services alone constitute about 15% of all the world's internet bandwidth! Not bad
for a company that started by posting DVDs by snail mail.
When it first opened, Netflix was purely a movie rental service. Users ordered movies on the Netflix website and received DVDs in the post. When they were finished with them, they would simply post them back to Netflix in the envelopes provided. At the time, this was seen as a boon to those who did not have a video rental store nearby (remember those?).
Today, Netflix streams movies and has more than 151 million paid subscribers in over 190 countries around the world. It offers a wide range of TV series, documentaries, and feature films across a wide variety of genres and languages, including original productions.