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 : 

  1. Advantages of recommendation system
  2. How does a recommendation system work
  3. Different types of filtering and segmentations used in a recommendation system
  4. And a brief summary on whether you should ‘Buy or Build a Recommendation System’


To download, simply fill out this form.

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Let’s uncover the past, navigate the present, and chart the course for what’s next in the dynamic world of streaming! In this webinar, we’ll embark on a journey through the dynamic landscape of streaming, exploring its roots, current state, and the exciting innovations shaping its future, all while uncovering the role of Muvi in this transformative journey.

Things the webinar will cover:

  1. Introduction to Streaming’s Origins:
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    • Milestones and key developments that laid the foundation.
  2. Current Streaming Landscape:
    • Overview of the current streaming ecosystem.
    • Market trends, consumer behavior, and the impact of global events.
  3. Muvi’s Contribution to Streaming:
    • How Muvi has played a pivotal role in the evolution of streaming.
    • Case studies and success stories highlighting Muvi’s impact.
  4. Technological Advancements:
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  6. Monetization Models:
    • Examining varied revenue streams in streaming.
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  7. User Experience and Engagement:
    • Enhancing user satisfaction and engagement.
    • Muvi’s features for creating a seamless streaming experience.
  8. Future Trends and Innovations:
    • Predictions for the future of streaming.
    • Muvi’s roadmap and commitment to staying ahead of industry trends.

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December 07

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