Although it is one of the most widely used methods in recommender systems, Collaborative Filtering (CF) still has difficulties in modeling non-linear user-item interactions. Complementary to this, recently developed deep generative model variants (e.g., Variational Autoencoder (VAE)) allowing Bayesian inference and approximation of the variational posterior distributions in these models, have achieved promising performance improvement in many areas. However, the choices of variation distribution - e.g., the popular diagonal-covariance Gaussians - are insufficient to recover the true distributions, often resulting in biased maximum likelihood estimates of the model parameters. Aiming at more tractable and expressive variational families, in this work we extend the flow-based generative model to CF for modeling implicit feedbacks. We present the Collaborative Autoregressive Flows (CAF) for the recommender system, transforming a simple initial density into more complex ones via a sequence of invertible transformations, until a desired level of complexity is attained. CAF is a non-linear probabilistic approach allowing uncertainty representation and exact tractability of latent-variable inference in item recommendations. Compared to the agnostic-presumed prior approximation used in existing deep generative recommendation approaches, CAF is more effective in estimating the probabilistic posterior and achieves better recommendation accuracy. We conducted extensive experimental evaluations demonstrating that CAF can capture more effective representation of latent factors, resulting in a substantial gain on recommendation compared to the state-of-the-art approaches.

中文翻译：

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通过协作自回归流进行推荐。

尽管它是推荐系统中使用最广泛的方法之一，但是协同过滤（CF）在建模非线性用户项交互方面仍然存在困难。与此互补的是，最近开发的深度生成模型变体（例如，变分自动编码器（VAE））允许在这些模型中进行贝叶斯推理和变分后验分布的近似，在许多领域都实现了令人鼓舞的性能改进。但是，变化分布的选择（例如，流行的对角协方差高斯分布）不足以恢复真实分布，通常会导致模型参数的最大似然估计有偏差。针对更具易处理性和表达性的变体族，在这项工作中，我们将基于流的生成模型扩展到CF，以对隐式反馈进行建模。我们介绍了推荐系统的协作自回归流（CAF），通过一系列可逆转换将简单的初始密度转换为更复杂的密度，直到达到所需的复杂度。CAF是一种非线性概率方法，允许不确定性表示和项目建议中潜在变量推断的精确易处理性。与现有的深度生成推荐方法中使用的不可知论推定的先验近似相比，CAF在估计概率后验方面更有效，并且可以实现更好的推荐准确性。我们进行了广泛的实验评估，表明CAF可以捕获潜在因素的更有效表示，与最新方法相比，可大幅提高推荐率。