asgardian28

An second thought the rank percentile would be heavily influenced by the amount of ratings given per user. If you rate only 3 games you think are great with high ratings, then one of them is going to recieve an awful percentile.

The same 3 great games rated by someone with 5000 ratings will have much 'better' percentiles, just because all the crap games.

And all the shades in between.

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.

That was the reason, had never heard of it. Sounds really fun though!

Actually I'm making a small application that shows similarly rated games. For fun tried out Brass: Lancastershire and got as most similar games: Brass: Birmingham, Age of Steam, Food Chain Magnate, Dominant Species

Top 3 for Eastfront: Advanced Squad Leader: Starter Kit #2, Unconditional Surrender! World War 2 in Europe and..... A Victory Lost: Crisis in Ukraine 1942-1943

The rank percentile is a great suggestion and after that take the average rank I guess?

Ratings per year is a possibility.

Complexity: Indeed I wanted to remove some of the more complex games because I don't have time for them. Although on reddit there was a nice discussion that the weight is composed of complexity & depth