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: .

中文翻译：

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推荐系统生成模型的深度学习：一项调查


网络上海量信息的多样性促进了推荐系统（RS）领域的蓬勃发展。近年来，深度学习技术极大地影响了信息检索任务，包括 RS。神经网络的概率和非线性观点出现在推荐任务的生成模型中。目前，缺乏对 RS 深度生成模型的广泛调查。因此，本文旨在对 RS 深度生成模型的最新进展进行连贯且全面的调查。特别是，我们在设计 RS 深度生成模型的分类法方面进行了深入的研究工作，并总结了最先进的方法。最后，我们根据这个有趣且发展的领域的最新趋势和新研究途径强调了潜在的未来前景。本次调查涵盖的公共代码链接、论文和流行数据集可通过以下网址访问： 。