What are recommender systems?

A recommender system is a computer program that makes predictions about how much a certain user will like something. For example, a recommender system could exist as part of an online store to suggest other products that you may like.

There are many different types of recommender systems as they tend to be designed with a very specific purpose in mind, such as music suggestions or movie suggestions. However, the basic concept is still the same – that a user’s likes and dislikes can be accurately gauged by looking at past data.

To predict what somebody will like in this way seems to be an easy task, however, it’s actually deceptively complex. This complexity is why the field of recommender systems has only started to blossom now that we are well and truly in the computer age.

The ability to accurately predicting user preferences is something that’s extremely attractive to businesses as it helps to drive efficiency and, in particular, can help to personalize marketing efforts. Because of this, it is a branch of AI that’s growing rapidly and becoming a crucial element of many companies’ marketing strategies.

Why are recommender systems important?

Recommender systems are now widely used in a range of industries. Being able to predict exactly what any given individual will like is the holy grail of marketing. It means products and services can be targeted very efficiently, cutting down the need for marketing expenditure drastically and saving a great deal of time and money in terms of human labor.

The most prominent applications of recommender systems today are in industries like retail, eCommerce, and on-demand video or music services. These companies use the systems to track what people are buying, watching, or listening to and then suggest additional products and content for them. If executed accurately enough, this leads to greater user engagement and, ultimately, increased profits.

“Being able to predict exactly what any given individual will like is the holy grail of marketing.”

However, it’s not simple to build and run these systems. The process usually involves a large number of experts like data scientists, software engineers, and AI experts. A lot of technology and expertise is behind the systems, with advanced machine learning algorithms now regularly used to drive them.

These machine learning algorithms can generally be broken down into two main branches – collaborative and content-based.

Collaborative algorithms use information about likes and ratings from the current user as well as other users. This helps to build a picture of what the current user is likely to respond positively to. Content-based systems focus on a user’s or product’s features, such as demographics, to construct a profile for that user or product. This profile can then be used to make suggestions, which become more accurate over time. The most modern examples of machine learning algorithms for recommender systems may combine these two branches to make an even more effective hybrid approach.

Because of the inherent complexity, even large corporations with enormous budgets do not usually build the systems themselves but outsource to specialized providers who provide better value. This is doubly true for SMEs that lack the budgets and expertise necessary to do the work in-house.

Use cases

eCommerce

eCommerce companies rely on selling as many products as possible online. To these businesses, it’s important to help users sift through the massive amount of products that may be available. Take Amazon as an example. There are hundreds of thousands of products listed on Amazon, so it can become overwhelming for users who are browsing. Recommender systems help Amazon and other eCommerce businesses to better understand what individual users will like. This makes it easier to show them products they’ll be interested which in turn leads to increased sales.

Streaming services

The explosion of streaming services such as Youtube, Netflix, and Spotify has taken the internet by storm. These are now among the most highly valued companies on earth, and their success is mostly due to their convenience for users. People can log in and immediately be shown lots of content that is likely to appeal to them, which keeps them clicking and keeps the revenue flowing. Recommender systems are integral to the success of these companies, who continue to be some of the biggest investors in the technology as a result.

Social media

Many big players in social media such as Facebook, Pinterest, and LinkedIn utilize recommender systems to keep users coming back for more. The personal feeds of such social media interfaces have evolved considerably in recent years, and this can largely be attributed to the intelligent use of recommender systems. Videos, images, and news stories are displayed in a way that’s completely unique for every user, based on their own activity and that of others, helping to create a more personalized feel.

Find out exactly what your customers want

Recommender systems are having a growing impact on the way all of us use the internet, and they can transform the way companies operate.

Sciling is made up of artificial intelligence experts with proven success in a mixture of academic research and commercial applications. Our decades of experience working with machine learning technologies allow us to build customized software solutions that help businesses like yours to understand exactly what your customers want.

Get in touch to find out how you can benefit from machine learning technology.

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