3 ms·
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
by sWW26 5y ago
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/ https://trythesegames.com/
[1] https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2019.01317.x https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713....
[2] https://trythesegames.com/rankings https://trythesegames.com/rankings
[3] https://boardgamegeek.com/thread/2728398/flexible-boardgame-ranking-system https://boardgamegeek.com/thread/2728398/flexible-boardgame-...
- asgardian28 5y agoReally 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.