A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System

Understand

This paper introduces a system used to generate game feature suggestions based on a text prompt.

• Trained on the game descriptions of almost 60k games, it uses the word embeddings of a small GLoVe model to extract features and entities found in thematically similar games which are then passed through a generator model to generate new features for a user's prompt.
• We perform a short user study comparing the features generated from a fine-tuned GPT-2 model, a model using the ConceptNet, and human-authored game features.
• Although human suggestions won the overall majority of votes, the GPT-2 model outperformed the human suggestions in certain games.