Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, they do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a rich family of time-sensitive factorization models. They include Hierarchical RPF that captures the consumption heterogeneity among users and items, Dynamic RPF that handles dynamic user preferences and item specifications, Social RPF that models the social-aspect of product adoption, Item-Item RPF that considers the inter-item correlations, and eXtended Item-Item RPF that utilizes items’ metadata to better infer the correlation among engagement patterns of users with items. We also develop an efficient variational algorithm for approximate inference that scales up to massive datasets. We demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and wide variety of large scale real-world datasets.

中文翻译：

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时间推荐的循环泊松分解

Poisson Factorization (PF) 是具有隐式反馈的推荐系统的黄金标准框架，其变体在现实世界的推荐任务中表现出最先进的性能。然而，他们没有明确考虑用户的时间行为，这对于在正确的时间向正确的用户推荐正确的项目至关重要。在本文中，我们介绍了循环泊松分解 (RPF) 框架，该框架通过利用泊松过程对隐式反馈进行建模来概括经典 PF 方法。RPF 将时间视为模型的自然组成部分，并考虑了重要的推荐因素，以提供丰富的时间敏感分解模型。它们包括 Hierarchical RPF，它捕捉用户和物品之间的消费异质性，处理动态用户偏好和项目规范的动态 RPF，对产品采用的社会方面建模的社会 RPF，考虑项目间相关性的项目-项目 RPF，以及利用项目元数据更好地推断用户与项目的参与模式之间的相关性。我们还开发了一种用于近似推理的有效变分算法，该算法可扩展到海量数据集。我们在合成数据集和各种大规模现实世界数据集上展示了 RPF 优于许多最先进方法的优越性能。和 eXtended Item-Item RPF，它利用项目的元数据来更好地推断用户与项目的参与模式之间的相关性。我们还开发了一种用于近似推理的有效变分算法，该算法可扩展到海量数据集。我们在合成数据集和各种大规模现实世界数据集上展示了 RPF 优于许多最先进方法的优越性能。和 eXtended Item-Item RPF，它利用项目的元数据来更好地推断用户与项目的参与模式之间的相关性。我们还开发了一种用于近似推理的有效变分算法，该算法可扩展到海量数据集。我们在合成数据集和各种大规模现实世界数据集上展示了 RPF 优于许多最先进方法的优越性能。