Machine Learning

At some point this year, I let my laptop run flat-out for almost two weeks just to answer one question: how much of a four-player board game is “skill” and how much is “luck”? That sounds excessive, but there was a catch: before I could even start those simulations, I had to fix a basic problem. Elo – the rating system we’ve been happily using so far – only really knows how to handle one-on-one duels.

Whether a game counts as “skill” or “chance” isn’t just a pub argument — in many countries it’s a legal distinction. Roulette and blackjack live on the “chance” side; tennis and chess are filed under “skill”. Different rules, different taxes, different ways for people to lose money.

The trouble is that this line is usually drawn by tradition and gut feeling. Is poker really “more skill” than backgammon? Is snooker closer to roulette or closer to chess? A group of economists tried to answer that question more systematically: instead of arguing, measure how “skill-heavy” a game is in practice by looking at the Elo ratings of all its players. We’ll meet their work properly in a bit.

What is a good recommendation?

Collaborative filtering is the workhorse powering the recommendations by Recommend.Games. Over the years, I’ve been asked every now and then how it works. So, I thought it’s high time I outlined the basic ideas behind our recommendation engine.

Let’s first take a step back and talk about recommendations in general. What is it we’re trying to achieve? The answer to this question is far from trivial, and it gets harder when you want to formalise its goals. Maybe a somewhat naïve approach would be to say that we want to recommend items that the user will like. But recommendations are as much about predicting what the user wants as what they didn’t even know they wanted. Sometimes the most “correct” answer is also the least useful: maybe our #1 recommendation is Wingspan and the user indeed would love to play it - but if they already knew about it, why recommend it in the first place?