player2vec: A Language Modeling Approach to Understand Player Behavior in Games

arXiv:2404.04234v1 Announce Type: new
Abstract: Methods for learning latent user representations from historical behavior logs have gained traction for recommendation tasks in e-commerce, content streaming, and other settings. However, this area still remains relatively underexplored in video and mobile gaming contexts. In this work, we present a novel method for overcoming this limitation by extending a long-range Transformer model from the natural language processing domain to player behavior data. We discuss specifics of behavior tracking in games and propose preprocessing …

abstract arxiv behavior commerce cs.ai cs.cl cs.lg e-commerce games gaming however language logs mobile mobile gaming modeling novel recommendation streaming tasks type video work