That’s what led me to build Slated.gg.
Most game recommenders look at games from the outside (genre, themes, etc.) What mattered to me was what’s it like to play. How much focus it needs, what type of thinking it requires, can I play in short bursts, is it emotionally taxing, etc.
I built a game recommender around experience and landed on 30 experiential dimensions (so far). I created an AI pipeline that researches and analyzes games by those dimensions and stores them in a vector space. Think sentiment analysis, but for player experience. The recommender then finds similarities by geometric (Euclidean) distance based on other games you like.
When I mapped 297 games, it was really interesting to see what clusters emerged. AAA games cluster tightly together, exposing how they are built with the same formula. The edges are more interesting and surface games with real differentiation. These tend to be the kinds of games players have a hard time finding good recommendations for.
Visualization and methodology: https://slated.gg/map
Try it: https://slated.gg/discover-games
Happy to answer questions about the approach or where it falls short.