An easy way to understand recommendation engines in AI

12 december 2019 | 4min read

As we move into an era of data explosion, it is becoming more and more important to find ways to scan through the huge amount of data. Recommendation engines in AI are used for filtering data.

It makes sure that the customer gets to see the data that is only relevant for his or her taste, style and preferences and ensures he or she spends minimum time searching for the right information.

We will explain more about recommendation engines and why it is essential in the business context. The growing amount of information on the internet and a significant rise of users are just a few causes…

 

You will read about the next topics in this blog:

  • What is a recommendation system or engine in AI?
  • Definition of a recommendation engine.
  • What are the benefits of a recommendation engine?
  • Examples of recommendation engines in real life.
  • Recommendation engines for real estate websites.

 

What is a recommendation system or engine in AI?

A recommendation engine is a system that suggests information, products, services to visitors based on different data sources.

The recommendation can come from a variety of factors such as past user behavior, similar users, similar products and more.

In a world of with tons of information and product overload, a recommendation engine provides an efficient way to provide users with personalized information and solutions.

These systems are quickly becoming the best way for users to expose to the whole digital world through the lens of their experiences, behaviours, preferences and interests.

 

Definition of a recommendation engine

A recommendation engine is a system that identifies and provides recommended content or digital items for users. As mobile apps and other advances in technology continue to change the way users choose and utilize information, the recommendation engine is becoming an integral part of applications and software products.

Recommendation engines are also known as recommendation systems.

 

What are the benefits of a recommendation engine?

A recommendation engine has a lot of benefits: it can significantly boost revenues, conversions, click-through rates (CTRs) and more. Usually, it has a positive effect on the user experience, thus translating to higher customer satisfaction and retention. With an e-commerce business, you can also have important benefits like cross-selling and upselling.

One of the companies that uses a recommendation engine is Netflix. It has thousands of movies. But instead of having to browse through all those items, Netflix presents you with a much narrower selection of items that you are likely to enjoy. But they really take it to the next step by showing you personalized covers for each movie.

This possibility saves the customer time and a higher customer satisfaction. With this function alone, Netflix received lower cancellation rates. This saved the company around a billion dollars a year.

Recommender systems have been used for almost 20 years by companies like Amazon or Netflix. Industries such as finance, travel and real estate only started to use this during the last few years.

“ Recommendations Engines — one of the concepts in Artificial Intelligence is fast gaining momentum. It is a perfect marketer tool especially for e-commerce / online businesses and is very useful to increase turn around (sales, profits etc.) ”

Examples of recommendation engines in real life

Some examples of recommendation engine usage are seen in the following:

  • Facebook — “People You May Know”
  • Netflix — “Other Movies You May Enjoy”
  • Amazon — “Customer who bought this item also bought …”
  • Google — “Visually Similar Images”
  • YouTube — “Recommended Videos”
  • LinkedIn — “Jobs You May Be Interested In”
  • Waze — “Best Route”
  • Spotify – “best music”

 

Recommendation engines for real estate websites

Recommendation engines need a lot of data from your website (properties, user behavior, …) to be effective with their suggestions. The information they collect and integrate is a critical aspect of the process.

For real estate portals, this can be information related to user behavior, for example, viewed images, clicks and time on a page or other information about the profile such as past activity.

These can combine with all the data of the properties on a real estate website.

When a visitor uses the website a certain amount of time, a lot of properties that show up will be more personal. This is because the AI engines start to recognize certain patterns of the profile of the user.

The result of a recommendation engine could be more conversion, a boost of the user experience and higher click through rate (CTR).

These AI engines are finally starting to get used in the real estate world and will definitely be an upward trend towards the future…

More information about a recommendation engine for real estate websites here.

 

Conclusion

Recommendation Engine is your companion and advisor to help you make the right choices by providing you tailored options and creating a personalized experience.

Currently, it is seen in online retail and media industries. It is catching up in transportation, finance and healthcare. One of the most recent uses of recommendation engines is for real estate websites that offer thousands of properties.

It is beyond a doubt that recommendation engines are getting popular and critical in the new age of things. It is going to be in the best interest to learn to use recommendation engines for businesses to be more competitive and consumers to be more efficient.

In a nutshell, recommendation engines are a contemporary form of artificial intelligence at play.

 

References

https://medium.com/humansforai/recommendation-engines-e431b6b6b446

https://www.sciencedirect.com/science/article/pii/S1110866515000341

https://en.wikipedia.org/wiki/Recommender_system