Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence time owing to the quadratically increasing computational cost with respect to the sample size; it is problematic, particularly for large-scale datasets and complex models such as neural networks. By contrast, a pointwise approach does not directly solve a ranking problem, and is therefore inferior to a pairwise counterpart in top-K ranking tasks; however, it is generally advantageous in regards to the convergence time. This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. The key idea is to estimate the ranking of items in a pointwise manner; we first reformulate the conventional pointwise approach based on density ratio estimation and then incorporate the essence of ranking-oriented approaches (e.g. the pairwise approach) into our formulation. Through experiments on three real-world datasets, we demonstrate that our approach not only dramatically reduces the convergence time (one to two orders of magnitude faster) but also significantly improving the ranking performance.

中文翻译：

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基于密度比的隐式反馈个性化排名

从隐式用户反馈中学习是具有挑战性的，因为我们只能观察到积极的样本，而从不访问消极的样本。大多数常规方法通过采用带有负采样的成对排名方法来解决此问题。但是，成对排序方法由于在样本数量方面的计算成本呈二次方增加的趋势，因此在收敛时间上存在严重的缺点。这是有问题的，特别是对于大规模数据集和复杂模型（例如神经网络）而言。相比之下，逐点方法不能直接解决排名问题，因此在排在前K位的排名任务中不如配对方法。然而，在收敛时间方面通常是有利的。这项研究旨在建立一种从隐式反馈中学习个性化排名的方法，这协调了点对点方法的训练效率和成对对应点的排序效率。关键思想是以逐点方式估计项目的排名。我们首先基于密度比估计重新构造传统的逐点方法，然后将面向排名的方法（例如成对方法）的本质纳入我们的公式。通过对三个现实世界数据集的实验，我们证明了我们的方法不仅大大减少了收敛时间（快了一到两个数量级），而且还显着提高了排名性能。我们首先基于密度比估计重新构造传统的逐点方法，然后将面向排名的方法（例如成对方法）的本质纳入我们的公式。通过对三个现实世界数据集的实验，我们证明了我们的方法不仅大大减少了收敛时间（快了一到两个数量级），而且还显着提高了排名性能。我们首先基于密度比估计重新构造传统的逐点方法，然后将面向排名的方法（例如成对方法）的本质纳入我们的公式。通过对三个真实数据集的实验，我们证明了我们的方法不仅大大减少了收敛时间（快了一到两个数量级），而且还显着提高了排名性能。