Deep learning with the generative models for recommender systems: A survey

Abstract

The variety of enormous information on the web encourages the field of recommender systems (RS) to flourish. In recent times, deep learning techniques have significantly impacted information retrieval tasks, including RS. The probabilistic and non-linear views of neural networks emerge to generative models for recommendation tasks. At present, there is an absence of extensive survey on deep generative models for RS. Therefore, this article aims at providing a coherent and comprehensive survey on recent efforts on deep generative models for RS. In particular, we provide an in-depth research effort in devising the taxonomy of deep generative models for RS, along with the summary of state-of-art methods. Lastly, we highlight the potential future prospects based on recent trends and new research avenues in this interesting and developing field. Public code links, papers, and popular datasets covered in this survey are accessible at: https://github.com/creyesp/Awesome-recsys?tab=readme-ov-file#papers.