Recommender systems in retail

If you’ve ever bought something on Amazon or other large online retailers, you’ll have noticed the ‘similar products’ that the site recommends to you after you’ve made your purchase. Sometimes they’re not the best suggestion, but in my experience most of the time they hit the mark.

This is an area of machine learning called recommender systems.

How do recommender systems work? In the case of online retailers, the standard approach is to fill out huge matrices and work out the relationships between different products. You can then see which products normally go together in the same basket, and make recommendations accordingly. This is called collaborative filtering and it works mainly because most products have been purchased thousands or millions of times, allowing us to spot the patterns.

A table of products and how often they are bought together. This is the basis of collaborative filtering, which is one recommender systems algorithm.
A table of products and how often they are bought together. This is the basis of collaborative filtering, which is one recommender systems algorithm.

Recommender systems for dating

Now imagine you run a dating website. Let’s simplify and say your site only caters for male-female pairings. How do you recommend a female to a male user who’s just registered?

This is when things get tricky. There are many users, new users are registering all the time, and most users have made few contact requests.

In this case we can work with what we do have:

  • The user’s profile text
  • The profile photo
  • The contact requests, if any.

Recommender systems on text

One approach which I like to use is a deep learning approach called vector embeddings, which goes like this:

  • You can convert every profile text into a ‘fingerprint’. For example it could be a vector in 100-dimensional space.
  • The 100-dimensional vector by itself is meaningless, but people with similar tastes should end up with similar vectors.
  • If you want to make recommendations for a new user, you can calculate their vector, and the distance to other vectors, and find its nearest neighbours!
A dating recommender system might assign all users to a location in vector space, and match users who appear close together.
A dating recommender system might assign all users to a location in vector space, and match users who appear close together.

Of course the tricky bit is how to go from a profile text and image, to a vector. This is something that Convolutional Neural Networks (CNNs) are very good at.

Applications of text based recommender systems in other industries

Vector embeddings can be useful for making recommendations in other industries too:

  • Recruitment websites, where candidates have uploaded a CV and you want to recommend jobs.
  • Property sales, where you have a description of the house and a photo.
A house sale website recommender system might need to make use of text and image data
A house sale website recommender system might need to make use of text and image data

There are off the shelf recommender systems that you can use for online retail or movie recommendations. But for text or image based recommendations really you need a custom solution, and this is extremely complex to build.

I have set up Fast Data Science Ltd to provide consulting services in this area after 10 years’ experience working with machine learning on natural language data. If you have lots of text or image data and you’d like to build a custom recommender system I’d love to hear from you. Please contact me here or write a comment below.

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