Whitepaper
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
Recommendation Systems
QuickStar ML Blueprints
30 May 2023

Want more? Check our articles

data enrichtment flink sql using http connector flink getindata big data blog notext
Tutorial

Data Enrichment in Flink SQL using HTTP Connector For Flink - Part Two

In part one of this blog post series, we have presented a business use case which inspired us to create an HTTP connector for Flink SQL. The use case…

Read more
lean big data 1
Tutorial

Lean Big Data - How to avoid wasting money with Big Data technologies and get some ROI

During my 6-year Hadoop adventure, I had an opportunity to work with Big Data technologies at several companies ranging from fast-growing startups (e…

Read more
managingmultipledatasourceobszar roboczy 1 4
Tutorial

Feature store - managing multiple data sources with Feast

As the effort to productionize ML workflows is growing, feature stores are also growing in importance. Their job is to provide standardized and up-to…

Read more
5mlopsobszar roboczy 1 4
Tutorial

MLOps: 5 Machine Learning problems resulting in ineffective use of data

In recent times, Machine Learning has seen a surge in popularity. From Google to tech startups, everyone is rushing to use Machine Learning to expand…

Read more
highly available airflow cluster aws notext
Tutorial

Highly available Airflow cluster in Amazon AWS

These days, companies getting into Big Data are granted to compose their set of technologies from a huge variety of available solutions. Even though…

Read more
why big data projects fail blog

Why do Big Data projects fail? Part I. The Business Perspective.

In a recent post on out Big Data blog, "Big Data for E-commerce", I wrote about how Big Data solutions are becoming indispensable in modern business…

Read more

Contact us

Interested in our solutions?
Contact us!

Together, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.


What did you find most impressive about GetInData?

They did a very good job in finding people that fitted in Acast both technically as well as culturally.
Type the form or send a e-mail: hello@getindata.com
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.

By submitting this form, you agree to our Terms & Conditions and Privacy Policy