Data Science

Recommendation System: How to Create One Using Machine Learning

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By Pere Munar, on 12 July 2023

If you’re involved in ecommerce, this article is for you! Imagine yourself browsing through platforms like Amazon, Netflix, or Spotify. You often come across recommendations for products that catch your interest, movies or series you might enjoy, or music that matches your taste. Well, these recommendations aren’t random. They are part of what is known as data science recommendation systems, which many companies implement for numerous benefits.

This article will delve into this fascinating world and guide you through the step-by-step process of creating your own recommendation system.

* Are you thinking about how to apply Data Science in your company? Click  here and contact us for a consultancy. We will help you determine if this tool  fits with your objectives and analyze how it can benefit your brand.

Recommendation System How to Create One Using Machine Learning


What Are Recommender Systems?

Recommendation systems are algorithms designed to predict the products or services in an online store that a user is most likely to purchase. These predictions are then displayed on the website while the user is browsing.

Before the development of machine learning, ecommerce platforms relied on showcasing "most purchased" or "top-rated" lists to attract consumers. However, these sections displayed the same items and services to all users. While these lists are still in use, recommender systems have proven to be more effective by providing personalized suggestions tailored to each individual customer.


How Do Recommendation Systems Work?

Recommendation systems analyze data collected from users’ browsing activities, such as the products they have viewed or purchased and their interaction with the platform. These systems use advanced algorithms to make detailed comparisons between user profiles to identify common patterns. Consequently, they can recommend products or servicing that become increasingly relevant to each consumer.


Types of Recommenders

When it comes to creating recommendation systems, experts typically employ two main strategies:

  • Collaborative Filters Recommenders: These algorithms focus on the user’s characteristics from information collected about them. The algorithm considers previous purchases, product ratings, average spending per purchase, and preferences. It then identifies similar users who have made comparable choices and determines which products or services they would like. Based on this analysis, the algorithm provides personalized recommendations.
  • Content-Based Filtering Recommenders: In this approach, the prediction is based on the characteristics of the product or service, and the user's purchasing history or preferences are not considered. Instead, the algorithm examines the product's features, such as price, brand, rating, size, and other relevant attributes, to generate recommendations.


    Types of Recommendations


Why Implement Recommendation Systems in Your Ecommerce?

  • Increase the likelihood of additional purchases: Encourage customers to discover and purchase more products and services, increasing ecommerce sales revenue.
  • Maximize the overall sales: Optimize product visibility and increase sales leading to higher conversion rates.
  • Retain customers longer: Keep them engaged within your online store, reducing their chances of leaving and increasing their potential customer lifetime value.
  • Boost customer satisfaction: Recommending products that align with customers’ interests and preferences enhances their shopping experience.
  • Foster customer loyalty: When customers feel understood and are provided with valuable recommendations, they are likelier to remain loyal to your business.


When Not to Implement a Machine Learning Recommendation System

While recommendation systems offer numerous benefits, it may not be the best time to implement them in your business if your customer base is small or your product or service catalog is limited. These factors can limit the algorithm’s effectiveness. Investing in data science becomes more profitable as your customer base grows and your offerings expand.


How to Create a Recommendation System With Machine Learning

Python is widely favored for creating data science and machine learning tools and web applications due to its robust code and optimized syntax. It is recommended for programmers entering this field due to its reliability and extensive software development support.

However, alternative languages such as Java, Golang, Node.js, PHP, or Ruby can also be considered.

Java is the best alternative to Python and its main competitor.

If you want to implement a web recommendation system or improve the one you already have, our data science team can help you. Contact us if you like us to analyze your situation.


Tips to Improve Your Recommendation System


Consider the Location

The placement of recommendations within your ecommerce matters. Take into account where and when the recommendations appear to optimize both the functionality of the system and the user experience.

The ideal location may vary depending on your website and the type of products or services you offer. However, standard practices in ecommerce include displaying recommendations at the bottom of the article or at the end of the purchase process.

If you need more clarification, we recommend doing A/B tests to make the best decision.


Strive for Strategic Relevance

What is a good recommendation? Well, the truth is that not every recommendation for the customer is good for your company.

While offering practical recommendations is crucial, some may be too obvious to be valuable to the customer. Therefore, consider introducing risky recommendations that expose customers to unfamiliar products and services.

From a business perspective, it is crucial to base recommendations on product profitability. The trick is to strike a balance between what benefits your business and what is valuable to the customer is key.

If you want to implement your web recommendation system or improve the one you already have, our data science team can help you. We hope we have helped you learn to implement web recommendation systems or improve the ones you already have with the tips and tricks!

Data science consulting with Cyberclick

Pere Munar

Data Scientist en Cyberclick. PhD en Astrofísica por la Universitat de Barcelona con más de diez años de experiencia en investigación mediante el análisis e interpretación de datos. En 2019 redirige su carrera profesional hacia el mundo del Data Science cursando el Postgrado en Data Science y Big Data de la UB, así como participando en el programa Science To Data Science (S2DS) en Londres. Actualmente forma parte del equipo de Data Science y SEM de Cyberclick.

Data Scientist at Cyberclick. PhD in Astrophysics from the University of Barcelona with more than ten years of research experience through data analysis and interpretation. In 2019 he redirected his professional career to the world of Data Science by graduating in Data Science and Big Data from the UB, as well as participating in the Science To Data Science (S2DS) program in London. He is currently part of Cyberclick's Data Science and SEM team.