Using deep learning and steam user data for better video game recommendations

Abstract

Recommendation systems have been used widely in many industries, including online retail, movies, and news media. Indeed, video game recommendation systems are one of the most important tools available to users and game distribution platforms today. A good recommender can help customers find games they might like faster, not only making it easier for them but also helping game distributors and developers to increase their sales and improve customer satisfaction ratings. One such platform that can greatly benefit from a recommendation system is Steam, the largest digital PC game distribution platform. Steam sees over a dozen million users login every day. It collects a considerable amount of data on each user, and this data may be used to help make better game recommendations. This paper proposes STEAMer, a solution for a new video game recommendation system for the Steam platform. STEAMer utilizes the Steam user data in conjunction with a deep autoencoder learning model to generate potential recommendations; we also apply the additional user data to an existing deep neural network-based recommendation system. Performance evaluation shows that the additional user data does indeed improve recommendation performance. Furthermore, when both systems use the additional user data, the deep autoencoder-based STEAMer still proves superior to the baseline deep neural network-based system in both mean average precision @ 10 (MAP@10) and normalized discounted cumulative gain @ 10 (NDCG@10) scores and in diversity.