3 min read

White Paper: Guide to Recommendation Systems

Our White Paper “Guide to Recommendation Systems” is already released. This article will give you a closer look at what you can find inside, what questions and problems it addresses, and some experts’ opinions.

Why should you read this White Paper

Have you ever wondered how recommendation systems work, how to implement them, what benefits bring, and how you should measure recommender systems' performance and business value? 

Well-working recommendation systems can increase engagement, retention, and revenue for businesses. However, building an effective recommendation system requires a deep understanding of user behavior and the machine learning models and techniques used to generate recommendations.

The white paper will teach you more about QuickStart ML Blueprints and its usefulness in developing recommendation systems. Whether you are a business owner seeking to implement a recommendation system, a data scientist studying the latest trends in the industry, or simply interested in learning how recommendation systems function, this comprehensive guide covers the complex and intriguing realm of recommendation systems.

Experts’ reviews about the White Paper

Recommendation systems fuel many companies, including tech giants like Amazon or Netflix. This whitepaper gives an excellent overview of two sides of the topic: business and technical aspects. It starts with a comprehensive explanation of how recommenders generate value for the business in different setups. It then goes smoothly into an intelligible description of more technical details, presenting simple baseline approaches and modern state-of-the-art architectures. It also brings two examples of such architectures created in GetInData as a part of the QuickStart ML Blueprints repository (formerly known as GID ML Framework) in the form of a working, well-polished codebase. Finally, here are some tips when considering implementing RecSys in your business.

**Piotr Chaberski, Senior Data Scientist**

I sincerely recommend this paper as Michał Stawikowski did a great job while conducting the research, and he summarized it into an understandable and valuable form.

I did not know all the accuracy metrics that can be used to evaluate the model, and in the paper, you can find clear reasoning behind applying each of them. While reading that part made me realize that Spotify's Discover Weekly recommendations must be using novelty measures.

**Adrian Dembek, Data Science Practice Lead**

Inside the Recommendation System White Paper you will find:

  • How to measure performance and business value of recommendation system  
  • A closer look how do recommendation systems works   
  • Four-Stage Recommender System example  
  • Develop your recommendation system with QuickStart ML Blueprints

What industries are most likely to use recommendation systems?

  • E-commerce 

Flagship example when it comes to achieving profits from the use of recommendation systems. Suggesting relevant products to end-users at multiple touchpoints sets online stores apart from their competitors and brings more sales.

  • Banking

Banks can try to better meet customers' expectations by offering personalized services, reduce the complexity of their choices, increase customer loyalty and ensure customer retention, and finally increase the frequency and also the overall value of the products they sell.

  • Telecom

Companies possess huge amounts of information. Allowing the customer to more easily discern the services offered and access more personalized offers can significantly reduce the cost of marketing campaigns, as well as ensure a steadily growing customer base.

  • Streaming services

This is an area that relies almost entirely on recommendations.

machine learning
Recommendation Systems
QuickStar ML Blueprints
30 May 2023

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