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
Beyond Netflix: Winning Europe’s Streaming M...
Streaming today goes far beyond building the next Netflix. From FAST channels and niche content platforms to live sports, audio streaming, short-form video, and enterprise content, businesses across Europe are discovering new ways to engage audiences and drive revenue. But what does it take to build and scale a successful streaming business? Join us for this live webinar as we explore Europe’s evolving streaming landscape, opportunities beyond traditional OTT, and the strategies businesses are using to launch, monetize, localize, and scale. Backed by real customer experiences, we’ll also discuss how AI is transforming content workflows and how Muvi helps streaming businesses accelerate their journey from idea to growth. Things we’ll cover: Streaming opportunities beyond the traditional Netflix-style OTT model Key trends shaping Europe’s video and audio streaming market Winning monetization strategies across SVOD, AVOD, FAST, PPV, and hybrid models Strategies to position and differentiate your platform in Europe’s fast-growing audio streaming ecosystem Real customer insights and experiences from European streaming businesses How AI is transforming content localization, metadata, accessibility, and streaming workflows Navigating content compliance with AI-powered solutions such as TrueComply Practical strategies for launching and scaling streaming services in Europe with Muvi About the Speaker Gaurav More, Senior Manager, Sales, Muvi Gaurav is a Senior Business Development Manager at Muvi, specializing in driving revenue growth and forging strategic partnerships across Europe and the Middle East. He possesses expertise in sales strategy, account-based marketing, lead generation, and customer relationship management within the SaaS and OTT industry. At Muvi, Gaurav plays a pivotal role in converting leads into paying customers, optimizing sales pipelines, and collaborating with cross-functional teams to deliver value-driven solutions. His strengths include executing structured outreach, conducting tailored product demonstrations, adopting a consultative approach, and identifying upsell opportunities to maximize customer retention and lifetime value.
August 27
12:00 PM EDT
