[praash]

This analysis is clever! I'd love to see someone use these ideas in an interactive tool for board game discovery.

Geeks appreciating complexity is obvious, but it's very interesting to see how much it actually matters in ratings. Some mechanic complexity is essential for replayability and depth, but maximizing the potential of individual components is a sign of great game design. Many successful games usually fit the saying "easy to learn, hard to master". Good games bring joy to pretty much anyone.

[sWW26]

I built a recommendation engine[0] using the BoardGameGeek data with the methods described in Significance[1].

I've also extended it[2] relatively recently to have a tweakable ranking system which my father explains here[3]

[0] https://trythesegames.com/ [1] https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.... [2] https://trythesegames.com/rankings [3] https://boardgamegeek.com/thread/2728398/flexible-boardgame-...

[asgardian28]

Really nice! As an extension to the article, I'm also making a recommender, but just colab filtering. But yours looks stellar! And the article is great, compliments!

Need some time to let the like score calculation sink in :-) I'm going to experiment with the (rating * 2) / 100, seems like a great way to account for the nonlinearity. Btw don't you divide by 10 instead of 100?

Another suggestion was to take transform the ratings of each user to percentiles, as a measure of how favorite the game is to the user, also seems interesting.