I've been thinking about a new app idea, and it has to do with matching or simple human behavior models. One potential idea is a gamble app in which each user will have multiple trials and gamble with a set amount of money. Each time the app will change up the condition (such as how much the player can win and the percent chance) and the computer will learn the behavior each time and predict what the next outcome will be. At the end it will show how much the user is risk averse or risk loving and how accurate the predictions were. I think it's a simple way to dive into machine learning and modeling human decision making which will be kind of cool. Another potential idea will be predicting if a song will be liked by a user or not. This will kind of be like tinder for music but each time the computer will suggest a song that is more likely to be liked by the user. Wouldn't it be cooler if Tinder actually did that, like started to learn what kind of people you like (say if someone liked people that wear glasses and are skinny) and gave you suggestions based on your type? Anyway the algorithm for that will be super simplified but solely based on user preferences and not song category or any other predetermined factor. Anyway, I thought it would be necessary to do some reading so I found a pretty cool scientific article called Human Matching Behavior in Social Networks: An Algorithm Perspective which is worth a read