Poll results are in!

Here’s how the community voted:

• Quake 4 -> 2 votes
• Quake III Arena -> 1 vote
• Aliens versus Predator Classic 2000 –> 1 vote

I then ran the same three games through the Gamescovery similarity algorithm to see which one is closest to the original Half‑Life.

According to the algorithm, Quake 4 takes the top spot, followed by Quake III Arena, with AVP 2000 coming in last.

So this time, the algorithm lined up with the community’s pick - but there’s a catch. Several people pointed out that Quake II might actually be the closest match to Half‑Life. In the algorithm, however, Quake II ranks even further away than AVP 2000.

I’ll double‑check that. Thanks to everyone who voted and shared their thoughts!

Hi, about Quake 2 - do you mean it was the most similar to the original Half-Life because of style? Or rather because of the main “idea”, genre, and plot? Just interested in your opinion

1:1

Willst du, dass ein Nerd seinen Micro-USB-Typ-C-Stick in dich reinsteckt? Iiiih, Alter.

Hab vor dem Meme noch nie was von dem Charakter gehört. Ist der aus 'nem Game oder Anime?

Hi. If you are still interested in Gamescovery, you can join our community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

Hi. If you are still interested in Gamescovery, you can join our community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

Hi. If you are still interested in Gamescovery, you can join our community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

Hi. If you are still interested in Gamescovery, you can join our community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

Hi. If you are still interested in Gamescovery, you can join our community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

You can join Gamescovery community and track the project’s status here:

• Matrix -> https://matrix.to/#/#gamescovery:gamescovery
• Lemmy -> https://lemmy.org/c/Gamescovery

Hi, good to hear, hopefully you will find Gamescovery useful.

Unfortunately, for now, I received a bunch of feedback, and I am implementing the needed stuff for the beta. For now, Gamescovery servers are disabled. I will let you know once the beta is online so you can test things by yourself.

deleted by creator

Hi, hopefully you will participate in beta I plan in a few months.

For now I gathered a bit of useful feedback and on my way to implement improvements.

I will let you know here when it will be available so you can join and try it by yourself

Hi, sorry for late reply.

For now I gathered a bit of useful feedback and on my way to implement improvements.

I’m aiming to release a beta version of the Gamescovery in 4-5 months. I will let you know here when it will be available so you can join and try it by yourself

I see, thanks for your opinion, I will definitely take that into account. I also find Criticker not that useful, although I have less rates than you have (around 400 rated things). And as you, I also believe that trying to compare me to other people to find “commons” - it’s not the best ways ot do recommendations.

Thanks again for that 3-4-3 system, I started doing my research yesterday, pretty sure it will end up implemented in Gamescovery until the beta.

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.

I’m thinking about using game database sites like https://rawg.io/, but I need to plan things and compare pros and cons before doing this.

Hi, thanks for the idea.

From my point of view, it’s not that complex to grab data from game stores and put it into my database. For services without insane rate limiting, I can pull 100k games in a few hours. Storing all the needed data is also not a problem.

The biggest problem with game data for now is to distill it and calculate differences, and this is what takes the most time and computing resources. Currently, Gamescovery DB has 70k+ games from itch.io and I spend a lot of time filtering it than on grabbing data from itch.io (originally, I grabbed around 100k games from itch.io).

So, when the right time comes, Gamescovery will have games from Steam without any complex hacks. I’m not sure about GOG and EGS, didn’t check that.

eg the modern 6/10=bad syndrome

I also saw a solution for normalizing the scores of every person to battle this bias. This is used in Criticker system (recommendation for movies):

https://www.criticker.com/critickers-algorithm-explained/

Once you've got enough ratings, Criticker normalizes your scores. We do this because of the wide variety in the way people rate things. For some people, a score of "80" is close to a masterpiece, while for others "80" is middling. Some try to spread their scores equally across the whole scale, while others adhere to the system they learned in school, when a "60" was real bad. Like, if you came home with a "60" on your math test, you were probably going to get grounded.

Currently, I’m investigating if this is something my recommendation algorithm needs, some maybe I will implement some kind of normalization for scores on Gamescovery, we will see.