Recommendation Engine

What is a recommendation engine?

A recommendation engine uses data to recommend relevant items to the user. In other words, recommendation engines use customer data analysis to match up a particular user with content or products they’ll love.

To do this successfully, a recommendation engine has to accurately predict what types of content a user will interact with based on their demographic characteristics and past behavior. Some factors that recommendation engines consider are:

  • Individual user behavior: After tracking on-site user actions like products/categories browsed, pages viewed, and time spent on site, recommendation engines use this customer data on a specific customer to send recommendations to that customer.
  • User similarity: Some recommendation engines collect data from all the users it has access to in order to create recommendations for groups of users with similar interests.
  • Product rankings: A recommendation engine works by tracking how popular items are, and customer ratings are an important aspect of this.

Many recommendation engines use artificial intelligence or machine learning algorithms to recommend items to customers. Generally, the bigger the data set a recommendation engine has, the more accurate recommendations it will make.

Types of recommendation engines

There are 3 different types of recommendation engines: collaborative filtering, content-based filtering, and hybrid systems. Let’s examine them one by one!

1. Collaborative filtering

Collaborative filtering uses behavioral data to determine the user’s taste based on their preferences and interests. Importantly, these connections between different items are based on everyone’s user behavior and user feedback, not just the individual the recommendations are for.

For example, recommendations might be made based on reasoning like this: “people who watched this show also liked this one.” Or you have a large selection of movies, and there are a set of users who rated them. Now the engine can use this information to recommend similar items to a user based on the items they have rated highly. So if a user rated highly the movie “The Shawshank Redemption” and “The Godfather”, we can recommend other movies like “The Dark Knight” or “The Goodfellas” which are similar to these in terms of plot, genre, director, etc. and would be enjoyed by the user.

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2. Content-based filtering

Content-based filtering is based on similarity in the content itself. This type of recommendation engine classifies different content according to type or genre.

Content-based filtering methods can also be applied to other types of items, such as books, songs, or articles. This type of filtering is effective when the user has rated some items in the past, which the system use as a basis to make future recommendations.

For example, if you watch a movie like the Dark Knight, you’ll get recommended movies that are similar to it, like other superhero movies or movies made by Christopher Nolan.

Unlike collaborative filtering, content-based filtering doesn’t rely on data about other users. Instead, it’s simply based on the features of the items and the customer’s own behavior.

Apple Music is great for content-based filtering, based on your music preferences, and what you have listened to recently. 

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3. Hybrid systems

Hybrid recommendation engines consider both collaborative data and content-based data when making recommendations. Essentially, a hybrid recommendation engine takes the best from both types of recommendation systems.

These engines offer content based on both your interest in certain types of content and on what other users think about that content. Netflix is a great example of hybrid system as the site makes recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based filtering)

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4 real-life examples of recommendation engines 

Here are some good examples of recommendation engines in action.

1. Netflix

Netflix—as it was mentioned before—has a famous recommendation engine that uses a hybrid system to suggest “Other movies you might enjoy.”

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2. Amazon

Amazon also boasts a highly effective recommendation system, with 35% of their total sales coming as a result of it. Their “Customers who bought this item also bought …” recommendation is an example of collaborative filtering.

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3. LinkedIn

Linkedin also uses a recommendation engine to recommend posts and jobs to their users.

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4. Apple Music and Spotify

Both Apple Music and Spotify have mastered the art of recommending new songs to their users. Spotify has their “Discover Weekly” playlist and Apple music always shows you “More like this.” Both these services monitor your listening and taste so that they can continually recommend different artists and content.

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