In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss. We show the minimization problem involves dependent random variables and provide a theoretical analysis by proving the consistency of the empirical risk minimization in the worst case where all users choose a minimal number of positive and negative items. We further derive a Neural-Network model that jointly learns a new representation of users and items in an embedded space as well as the preference relation of users over the pairs of items. The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users’ preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering. The proposed model is by nature suitable for implicit feedback and involves the estimation of only very few parameters. Through extensive experiments on several real-world benchmarks on implicit data, we show the interest of learning the preference and the embedding simultaneously when compared to learning those separately. We also demonstrate that our approach is very competitive with the best state-of-the-art collaborative filtering techniques proposed for implicit feedback.

在本文中，我们提出了一种用于协作过滤的新颖排名框架，其总体目标是通过最小化成对排名损失来学习用户对项目的偏好。我们显示最小化问题涉及因变量，并通过证明在所有用户选择最少数量的正负项目的最坏情况下经验风险最小化的一致性来提供理论分析。我们进一步推导了一个神经网络模型，该模型可以共同学习嵌入式空间中用户和项目的新表示以及用户对项目对的偏好关系。学习目标基于三种对损失进行排名的情况，这些情况控制着模型保持对用户偏好所导致的项目进行排序的能力，以及 在所学习的嵌入空间中定义的点积产生订单的能力。所提出的模型本质上适合于隐式反馈，并且仅涉及很少参数的估计。通过在多个隐式数据的现实世界基准上进行的广泛实验，与分别学习偏好和嵌入相比，我们表现出了同时学习偏好和嵌入的兴趣。我们还证明了我们的方法与针对隐式反馈提出的最佳最新协作过滤技术极具竞争力。与分别学习偏好和嵌入相比，我们显示了同时学习偏好和嵌入的兴趣。我们还证明了我们的方法与针对隐式反馈提出的最佳最新协作过滤技术极具竞争力。与分别学习偏好和嵌入相比，我们显示了同时学习偏好和嵌入的兴趣。我们还证明了我们的方法与针对隐式反馈提出的最佳最新协作过滤技术极具竞争力。