Learning to estimate prices of video games

Thesis Type Bachelor
Thesis Status
Currently running
Student Nikita Grimm
Thesis Supervisor

Traditional video game reviews and scoring are unable to account for personal tastes and the financial situation of a given user. This could lead to a user being dissatisfied with a game purchase if it was either too expensive for what it offered or unsuited to his tastes. To solve this problem and to give better recommendations, a neural network can be used to evaluate the price at which a game becomes worth buying to a given user. The goal of this thesis is to devise and train a neural network on a game’s properties, such as its developer, genre, release platforms and price, to achieve this goal. As target data, previously given user votes on what a fair price for a given game is will be used.