3 min readfrom Data Science

Steam Recommend pt 2 (Student Project)

Our take

Introducing the sequel to my Steam Game recommender website, designed to enhance your gaming experience! Building on last year’s success, this new version offers a more functional approach to discovering games tailored to your unique preferences. By analyzing over 2,000 reviews from 80,000 Steam games, I’ve created a system that breaks down games into specific vectors and tags, revealing what makes them enjoyable. This method not only uncovers hidden gems but also explains the rationale behind each recommendation.
Steam Recommend pt 2 (Student Project)
Steam Recommend pt 2 (Student Project)

I Just made a sequel to my Steam Game recommender website!

Last year I made a post about my steam reccomender The last one was great but this one I'm glad I was able to make a product that hopefully helped people find their next game. After some developing I made a new one that is much more functional!

I love making recommendation systems that tell the user WHY they got the recommendation.

During a steam sale event, I always find myself trying to look for new video games to play. If I wanted to find a new game I would try to whittle it down by using steam tags, but the steam tag system is very broad "action". could apply to many many games.

That got me thinking, what aspects do I like about my favorite games?
Well I like Persona 4 because of the city vibes and jazz fusion,
I like Spore because of the unique character creation and whimsical theme.
and I like Balatro for its unique deck building synergies.

What if I could capture unique tags that identify a game that aren't just "action" and put them into vectors to show the (focus) of a game

For example I could break persona 4 into something like

Gameplay Focus vector:
- Day cycle 20%
- Dungeon crawling 20%
- Social sim 20%

Tags:
- Music: jazz fusion
- Vibe: Small rural town

I achieved this by pulling 2k reviews for 80k steam games, running them through a 4 stage pipeline that filters out the reviews to find reviews describing a video game's vibes or structure, then asking chatgpt to generate these reviews into vectors, niche anchor tags and micro tags using non canonical names.

Then I used a 6 stage pipeline to group these non canonical names together (fast combat = speedy action combat)

From that I stored it all in PostgreSQL + Chroma db, made an app using React. and Shipped it all within a docker container inside a digital ocean droplet!

The result is a cool little steam game recommender that I can use to not just find similar games, but find games that share my favorite aspect of a game I like. A system that explains to me why I got the recommendations I got.

I find that this system makes searching for games more "fun" now I can see why I like balatro. I like it because of the card synergies not so much for its rogue-like nature.

I also find that this helps find new underrated games, and beats the trap that Collaborative Filtering algorithms that get into where it "feels" like you get recommended the same things.

find your next favorite game! : https://nextsteamgame.com/
pull a PR!: https://github.com/BakedSoups/NextSteamGame
( I actually made some git issues myself for problems I can't fix)
if anyone has any criticism I would love to hear it! this is probably my favorite passion project.

Hope this website helps people find new games! Also I have a advance mode for people that don't mind messing with sliders and weird data terms.

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