People are always short on time in this extremely busy world. When it’s time for a short break just to freshen up the mind, everyone prefers choosing from the options that are just a click away. I mean what could be better than a user being recommended with their favorite song to play, short movie to watch during a break. Yes, recommendation engines also learn your viewing patterns and may suggest content based on time of the day or your watching patterns. The recommender system helps users to find their preferred movies quickly on the home screen, without having to search from the extended content catalog.
A movie recommender system is basically a tool that helps streaming media platforms recommend users’ favorite movies on the basis of their interests and behavior. It creates a list of favorite movies according to the user profile. Using an AI-based algorithm that analyzes the data, it goes through various possible options, and creates a customized list of items that are interesting and relevant to an individual.
The results provided by a recommender engine are completely based on the user’s profile, search or browsing history, what other people are watching with similar traits/locations, and how likely is the user to watch those movies.
First, it takes a huge data set of movies as well as viewer ratings and then uses the collective ratings to break down individual movies into a long list of attributes. These resulting attributes then may resemble easily identifiable qualities such as comedy, cult classic, romantic, etc. but the computer only knows them as X, Y, Z.
Now the movie recommender system decodes individual tastes, and matching those tastes will recommend relevant movies. This is of course just one scenario discussed here.
There are thousands of movies available on every streaming media platform. A Recommender system helps to personalize a platform and help users find what they are looking for.
From a business perspective, the more relevant content or movies a user finds on any particular platform, the higher their engagement and as a result increased revenue. Various platforms have also revealed that 35 to 40% of revenue comes from recommendations only.
The movie recommender system lists out all popular movie genres according to the user’s taste and ensures high-quality recommendations. It recommends movies according to the mood as well as an occasion in the user’s locality.
Recommender system’s “Most Viewed” content recommendations rely on other users’ likes and views. The recommender system uses a content-based filtering method to comprehend what kind of content or movie most users watched in a particular genre and recommend them to the audience.
Customized recommendation feature allows streaming platforms to add a layer of filters to produce customized recommendations. OTT platforms can configure several filters to fine-tune recommendations and offer customized recommendations.
Businesses just need to upload the data set and generate recommendations for their users. After generating the recommendations, businesses can apply preferred filters to adjust the final recommendations.
“End-user interaction” recommendations depend upon the user’s past interaction on any particular platform. These interactions could be browsing history, views, ratings, etc.
Recommender systems use content-based filtering to comprehend a user’s behavior with the attributes to which they react positively.
Movie recommender system delivers a smart and personalized experience to users and in turn, helps streaming media service providers to enhance user engagement. It also provides perfect movie recommendations and helps users endlessly scroll through the movies and watch one after the other.
Muvi’s recommender system – Alie is an AI-powered recommendation system for websites and applications that easily integrates and provides a real-time recommendation to the users. Its distinct machine learning algorithm is designed to analyze user data and recommend personalized content with accuracy. If you have any doubt about how it works, consider taking a 14-days free trial now! This will help you gain knowledge of how our recommender engine helps recommend content to your end-users flawlessly.
Written by: Ankit Jena
Ankit is Content Writer for Muvi’s Marketing unit. He is a passionate writer with 5+ Years of Experience in Content Creation And Development. In his past time, he likes to dance, play football and google various things to quench his thirst for knowledge.
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