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
Build vs Buy in Streaming — The True Cost, Time ...
‘Building a streaming platform from scratch gives more control’ is a myth. In reality ‘build’ entails engineering, infrastructure, maintenance, compliance, upgrades, scaling, etc with additional cost barriers and time restraints. This webinar breaks down the real-world cost, time, and scalability implications of building vs buying a streaming platform, using a practical checklist approach. The session will help businesses cut through common myths around custom development and understand why many modern streaming businesses choose SaaS platforms like Muvi One to launch faster, reduce risk, and scale globally—without hiring large tech teams. Things the webinar would cover: What “building” a streaming platform actually involves today (engineering, infrastructure, maintenance, compliance, upgrades, scaling). The true cost of build vs buy, including hidden and long-term operational costs. Time-to-market realities like how long ‘buy’ vs ‘build’ realistically takes and the business impact of delayed launches. Scaling challenges across devices, geographies, and monetization models. A decision-making checklist businesses can use to assess readiness, risk, and ROI before choosing build or buy. How SaaS platforms like Muvi One enable faster, lower-risk, globally scalable streaming launches.
March 26
12:00 PM EDT
