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’
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AI in Streaming: Making Content Richer and Accessi...
Today, AI is helping streaming platforms make videos richer, more accessible, easier to discover, and more engaging. Join us for this live webinar to discover how AI is becoming an integral part of modern streaming workflows. Learn how streaming businesses are leveraging AI to enrich and multiply content, improve accessibility, expand global reach, and unlock greater value from every piece of content. During the session, we’ll also demonstrate how Muvi’s AI engine, Alie, brings these capabilities to life through AI-powered subtitles and translations, AI dubbing, Metadata generation, AI-powered search and chat experiences, etc. Things we’ll cover: The evolving role of AI in modern streaming platforms How AI enriches content and improves accessibility Using AI to enhance discoverability, engagement, and viewer experience Best practices for integrating AI into your streaming workflows Live demo of Muvi’s AI-powered capabilities including Muvi Flow
July 30
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