Personalized recommendations from the first click

The Challenge

The first view after authentication is crucial, especially when you serve content or products. But how do you show users something relevant when you know nothing about them? Asking for preferences feels like a compromise, knowing each onboarding step risks drop-off. Can you do better than just showing the most popular products?

The Solution

Gravatar provides user profile data as soon as a user shares their email. For users with a Gravatar account, you can access their interests.

1. Interests: -> explicit, added by the user to their Gravatar profile.

If your service handles keyword-based search well, Gravatar can already offer great recommendations out of box.

In some cases, you’ll need a translation layer to map general interest tags to your domain-specific keywords—a kind of context translation.

Example:
The interest “software engineer” might map differently depending on your domain.

In a fashion store: Hoodies, T-Shirts, Sneakers, Minimalist Bags

In a podcast app: Technology, Startups, AI, Coding, Open Source

The Prototype Idea

For this simple prototype, we imagined a podcasting service. Could we generate personalized podcast recommendations in the first view using only Gravatar profile data?

Reminder: We built the prototype in a single day, using publicly available API data, without time for fine-tuning the data sets, or building a context translation as mentioned above.

Even with such a primitive recommendation engine the prototype shows that personalization can offer a lot of value.

What if a user doesn’t have a Gravatar account?
No problem. They’ll see the same flow as before Gravatar was integrated. The only cost is a single API call.