A Survey on Representation Learning for User Modeling

Abstract

Artificial intelligent systems are changing every aspect of our daily life. In the past decades, numerous approaches have been developed to characterize user behavior, in order to deliver personalized experience to users in scenarios like online shopping or movie recommendation. This paper presents a comprehensive survey of recent advances in user modeling from the perspective of representation learning. In particular, we formulate user modeling as a process of learning latent representations for users. We discuss both the static and sequential representation learning methods for the purpose of user modeling, and review representative approaches in each category, such as matrix factorization, deep collaborative filtering, and recurrent neural networks. Both shallow and deep learning methods are reviewed and discussed. Finally, we conclude this survey and discuss a number of open research problems that would inspire further research in this field.