A Detailed Guide on AI-based Recommendation System
White paper on Recommendation System
Have you ever wondered how some websites know what you might want before you even think about it? For example, you wish to buy a book and visit an e-commerce website. Once you log in, you are greeted with a list of books from genres that you were going to buy. Or, you open an OTT platform to watch something, and are provided with suggestions of movies that may be of interest to you. Well, if that makes you ponder about techno-wizardry, let me assure you, this isn’t any magic, but an integrated recommendation system. Download the white paper on Recommendation System to understand better.
What is a Recommendation System?
A recommendation system collects and analyzes user behaviour data stored on a website to suggest the most appropriate recommendations to the users. Once the data is identified, it goes through several processes, such as data acquisition, data cleaning, data storage, and data analysis/filtering before the system churns out recommendations.
Uses of Recommendation System
Recommendations are needed in almost every industry, be it banking, travel, e-learning, or even gaming. Why? Because as the world is getting digitized, the push for instant gratification, on time and personalised delivery is becoming a necessity. Strategically inferring, adopting this technology, not only increases customer satisfaction and loyalty but also adds on to the revenue of an organization as, more often than not, customers end up purchasing the suggestions made by the recommendation engine. In fact, according to a report by Mckinsey, in the year 2014, Amazon’s 35 percent of total revenue came from their recommendation system.
With our latest whitepaper, we intend to showcase how the recommendation system works, its origin, and more. Additionally, you will also get to get a deep dive on :
- Advantages of recommendation system
- How does a recommendation system work
- Different types of filtering and segmentations used in a recommendation system
- And a brief summary on whether you should ‘Buy or Build a Recommendation System’
Access & Download Now
White paper on Recommendation System
Have you ever wondered how some websites know what you might want before you even think about it? For example, you wish to buy a book and visit an e-commerce website. Once you log in, you are greeted with a list of books from genres that you were going to buy. Or, you open an OTT platform to watch something, and are provided with suggestions of movies that may be of interest to you. Well, if that makes you ponder about techno-wizardry, let me assure you, this isn’t any magic, but an integrated recommendation system. Download the white paper on Recommendation System to understand better.
What is a Recommendation System?
A recommendation system collects and analyzes user behaviour data stored on a website to suggest the most appropriate recommendations to the users. Once the data is identified, it goes through several processes, such as data acquisition, data cleaning, data storage, and data analysis/filtering before the system churns out recommendations.
Uses of Recommendation System
Recommendations are needed in almost every industry, be it banking, travel, e-learning, or even gaming. Why? Because as the world is getting digitized, the push for instant gratification, on time and personalised delivery is becoming a necessity. Strategically inferring, adopting this technology, not only increases customer satisfaction and loyalty but also adds on to the revenue of an organization as, more often than not, customers end up purchasing the suggestions made by the recommendation engine. In fact, according to a report by Mckinsey, in the year 2014, Amazon’s 35 percent of total revenue came from their recommendation system.
With our latest whitepaper, we intend to showcase how the recommendation system works, its origin, and more. Additionally, you will also get to get a deep dive on :
- Advantages of recommendation system
- How does a recommendation system work
- Different types of filtering and segmentations used in a recommendation system
- And a brief summary on whether you should ‘Buy or Build a Recommendation System’
Upcoming Webinar
Launching OTT for Emerging Markets: Payments, Lang...
Emerging markets are driving the next wave of OTT growth—but succeeding in these regions requires unlocking various challenges. From fragmented payment ecosystems and diverse languages to inconsistent network infrastructure, video platforms must adapt strategically to unlock real scale. Join us for this webinar where we break down what it truly takes to build, launch, and scale OTT platforms in high-growth emerging markets like South & Southeast Asia. In this session, we’ll explore key market realities, and the critical technology and business decisions that can make or break your OTT success in these regions. We will also touch upon how platforms like Muvi One help overcome these challenges through integrated solutions for payments, localization, and streaming performance. Things the webinar would cover: Key growth trends, mobile-first consumption behavior, and what makes these markets unique. Navigating low credit card penetration with alternative payment methods like wallets, and carrier billing Leveraging AI for multi-language UI, automated subtitles and dubbing, along with intelligent content discovery, personalization, and predictive analytics to drive deeper regional engagement. Handling low bandwidth conditions, device fragmentation, and ensuring seamless playback with adaptive streaming
April 30
5:30 AM EDT
