In a general way, recommendation systems are algorithms aimed at suggesting relevant items to users (movies to watch, text to read, products to buy or anything else depending on industries). 

Recommendation systems aim to predict users’ interests and recommend product items that quite likely are interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales. 

Data required for Recommendation systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries, or from other knowledge about the users/items themselves. 

During the last few decades, with the rise of YouTube, Amazon, Netflix and many other such web services, Recommendation systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), Recommendation systems are today unavoidable in our daily online journeys. 


Why do we need Recommendation systems? 

Companies using Recommendation systems focus on increasing sales as a result of very personalized offers and an enhanced customer experience. 

Recommendations typically speed up searches and make it easier for users to access the content they’re interested in and surprise them with offers they would have never searched for. 

What is more, companies are able to gain and retain customers by sending out emails with links to new offers that meet the recipients’ interests, or suggestions of films and TV shows that suit their profiles. 

The user starts to feel known and understood and is more likely to buy additional products or consume more content. By knowing what a user wants, the company gains a competitive advantage and the threat of losing a customer to a competitor decreases. 

How does work? 

Recommendation systems function with two kinds of information: 

  • Characteristic information:  

This is information about items (keywords, categories, etc.) and users (preferences, profiles, etc.). 

  • User-item interactions:.  

This is information such as ratings, number of purchases, likes, etc.


Why Adding a Recommendation System to Your Website is Beneficial? 

So, what are the advantages of adding a Recommendation system to your website or software? 

Here’s a list of just a few: 

  1. Increase in sales thanks to personalized offers. 
  2. Enhanced customer experience. 
  3. More time spent on the platform. 
  4. Customer retention thanks to users feeling understood. 


Is it worth investing in a good recommendation system? 

A good way to answer this question is to look at how companies that have implemented such systems have fared: 

  • 35% of the purchases on Amazon are the result of their Recommendation system, according to McKinsey.
  • During the Chinese global shopping festival of November 11, 2016, Alibaba achieved growth of up to 20% of their conversion rate using personalized landing pages, according to Alizila.
  • Recommendations are responsible for 70% of the time people spend watching videos on YouTube.
  • 75% of what people are watching on Netflix comes from recommendations.