In one-class recommendation systems, the goal is to learn a model from a
small set of interacted users and items and then identify the
positively-related user-item pairs among a large number of pairs with unknown
interactions. Most previous loss functions rely on dissimilar pairs of users
and items, which are selected from the ones with unknown interactions, to
obtain better prediction performance. This strategy introduces several
challenges such as increasing training time and hurting the performance by
picking "similar pairs with the unknown interactions" as dissimilar pairs. In
this paper, the goal is to only use the similar set to train the models. We
point out three trivial solutions that the models converge to when they are
trained only on similar pairs: collapsed, partially collapsed, and shrinking
solutions. We propose two terms that can be added to the objective functions in
the literature to avoid these solutions. The first one is a hinge pairwise
distance loss that avoids the shrinking and collapsed solutions by keeping the
average pairwise distance of all the representations greater than a margin. The
second one is an orthogonality term that minimizes the correlation between the
dimensions of the representations and avoids the partially collapsed solution.
We conduct experiments on a variety of tasks on public and real-world datasets.
The results show that our approach using only similar pairs outperforms
state-of-the-art methods using similar pairs and a large number of dissimilar
pairs.

本研究提出了一种利用类似集合训练模型的方法，通过添加基于 hinge 和正交约束的两个新项到目标函数中，避免了训练过程中出现的收缩、部分收缩和崩溃问题，并在公共和真实数据集上的实验表明，在只使用类似对的情况下，该方法优于采用大量非类似对的最先进的方法。

使用 Hinge 对比距离损失和正交表示的单类别推荐系统

One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations

In this paper, we present a deep learning architecture which addresses the
problem of 3D semantic segmentation of unstructured point clouds. Compared to
previous work, we introduce grouping techniques which define point
neighborhoods in the initial world space and the learned feature space.
Neighborhoods are important as they allow to compute local or global point
features depending on the spatial extend of the neighborhood. Additionally, we
incorporate dedicated loss functions to further structure the learned point
feature space: the pairwise distance loss and the centroid loss. We show how to
apply these mechanisms to the task of 3D semantic segmentation of point clouds
and report state-of-the-art performance on indoor and outdoor datasets.

本文介绍了一种深度学习架构，用于解决非结构化点云的三维语义分割问题。与以往的工作相比，我们引入了点群聚合技术，定义了初始世界空间和学习特征空间中的点邻域，并结合专用损失函数，进一步构建了学习点特征空间。我们展示了如何将这些机制应用于点云的三维语义分割任务，并在室内和室外数据集上报告了最新性能。