Recommender Systems From Implicit Feedback Using TensorFlow Recommenders | by Dr. Robert Kübler | Nov, 2023

When customers don’t explicitly tell you what they want

Dr. Robert Kübler
Towards Data Science
Photo by Noom Peerapong on Unsplash

Making recommendations is actually not that hard. You just have to check how your customers rated your products, for example using 1 to 5 stars, and then train a regression model on top of it. Right?

A typical dataset that you would like to have. Image by the author.

Okay, we might have to deal with embeddings if we don’t have any numerical user or movie features, but we have seen how to do that in my earlier article:

We will also need embeddings in this article, so I suggest reading the article above before you continue.

However, sometimes we are not in the lucky position of having explicit user feedback, such as stars, thumbs up or down, or similar. This happens quite a lot in retail, where we know which customer bought which item, but not if they actually liked it. The only things we get from the customers are implicit signals about their interest in this product.

If they bought (watched, consumed, …) the product, they have showed interest in it. If not, they were maybe not interested, but maybe just didn’t know about it yet. We cannot tell.

This sounds like we can treat a classification problem. Interested = 1, not interested = 0. However, this is the small problem that we cannot be sure if a 0 (not interested) is really a zero. It can also be that the customer just never had the chance to buy it, but would actually like to.

Let us go back to the movies and assume that we don’t have any ratings. We only know which user watched which movie.

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This post originally appeared on TechToday.

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