Hey everyone,
Like many of you, I’ve spent more time hunting for my next game than actually playing. I’m frustrated with recommenders that just push popular titles, ignoring what makes my taste unique.
That’s why I’ve been building Gamescovery (games discovery!).
What is it?
Gamescovery is a new recommendation system designed specifically for games. The goal is simple: use your ratings from the games you’ve played to find hidden gems and perfect matches you’d otherwise miss.
Why it’s different:
- It’s not a generic engine. It’s being built from the ground up to understand what you love about games.
- Future updates will let you fine-tune recommendations based on what matters most to you (genre, mood, developer, etc.).
- We start by focusing on the incredible world of itch.io indie games to help you uncover amazing projects that big algorithms overlook.
This is where you come in.
The alpha is now live, and it’s very much an early build. I’m not a big company, I’m a solo developer who wants to build something the community actually finds useful. That’s why your feedback is crucial.
As an alpha tester, you’ll get:
- Early access to a tool designed to beat the “recommendation paradox.”
- A direct line to the developer to shape project’s future.
- The chance to help build a non-biased, community-driven platform.
Ready to try it out?
👉 Sign up for the alpha and start getting recommendations here: https://gamescovery.com/
Want to chat, suggest features, or report bugs? 🎮 Join our Discord community: https://discord.gg/brr7aYezMc
This project has and will always have a free tier. The dream is to support all major platforms, but we’re starting with itch.io to prove the concept.
Thanks for your time, and I’m excited to hear what you think!
I didn’t rate any games, just looked at what it would take and had some quick feedback to offer. Part of the issue with Itch is that to rate games, you have to first find things on itch, as well as find things that’d be representative so you might see how recs do. For testing something that isn’t going to do much right now, that’s a fair bit of trouble, especially since my key interest would be whether recommendations really take taste into account or use one of the usual shortcuts that either lump you into categories or fall prey to the “well everyone likes X so X” syndrome. Either of those would take a fair bit of data for me to put in, and a rather surprising amount of data for you to already have at such an early stage.
Hi, I see, hopefully you will be willing to participate in further testing (for example, in beta) when the project is in better shape.
The only reason I bring the current alpha to the public is to test the concept and see if people are interested in it at all. I spent around 1 year (1 year of time, not of working hours) to make the current alpha, and there is no sense in spending one more year on a project nobody actually wants. For now, feedback was somewhat positive, so I want to continue and see what I will build next.
The main idea of my recommendation algorithm is to calculate the unique test for every user. It doesn’t and wouldn’t compare the tastes of different users to calculate assumptions. I hate this, and those kinds of recommendation algorithms seem to never work for me. When doing my research on the beginning of the project, I found that such algorithms were first used for social media, but I don’t feel these algorithms are correct (as I feel it, I can’t prove this with real numbers for now).
So, hopefully, Gamescovery recommendation algorithms wouldn’t have biases like "well everyone likes X so X”, since it never tries to compare 2 or more users. Besides that, Gamescovery will allow users to tweak the algorithm so that users can actually customize it to make the algorithm better for them. That doesn’t mean users will be able to completely change the behavior of the algorithm, but rather direct it in a direction they want.