We consider the problem of training private recommendation models with access
to public item features. Training with Differential Privacy (DP) offers strong
privacy guarantees, at the expense of loss in recommendation quality. We show
that incorporating public item features during training can help mitigate this
loss in quality. We propose a general approach based on collective matrix
factorization (CMF), that works by simultaneously factorizing two matrices: the
user feedback matrix (representing sensitive data) and an item feature matrix
that encodes publicly available (non-sensitive) item information.
The method is conceptually simple, easy to tune, and highly scalable. It can
be applied to different types of public item data, including: (1) categorical
item features; (2) item-item similarities learned from public sources; and (3)
publicly available user feedback. Furthermore, these data modalities can be
collectively utilized to fully leverage public data.
Evaluating our method on a standard DP recommendation benchmark, we find that
using public item features significantly narrows the quality gap between
private models and their non-private counterparts. As privacy constraints
become more stringent, models rely more heavily on public side features for
recommendation. This results in a smooth transition from collaborative
filtering to item-based contextual recommendations.

利用公共物品特征在训练中，通过基于搜集矩阵分解的方法，可以有效缓解使用不同隐私保护技术训练的推荐模型的品质损失。

具有公共项目特征的私有矩阵分解

Private Matrix Factorization with Public Item Features

Semantic segmentation with limited annotations, such as weakly supervised
semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS),
is a challenging task that has attracted much attention recently. Most leading
WSSS methods employ a sophisticated multi-stage training strategy to estimate
pseudo-labels as precise as possible, but they suffer from high model
complexity. In contrast, there exists another research line that trains a
single network with image-level labels in one training cycle. However, such a
single-stage strategy often performs poorly because of the compounding effect
caused by inaccurate pseudo-label estimation. To address this issue, this paper
presents a Self-supervised Low-Rank Network (SLRNet) for single-stage WSSS and
SSSS. The SLRNet uses cross-view self-supervision, that is, it simultaneously
predicts several complementary attentive LR representations from different
views of an image to learn precise pseudo-labels. Specifically, we reformulate
the LR representation learning as a collective matrix factorization problem and
optimize it jointly with the network learning in an end-to-end manner. The
resulting LR representation deprecates noisy information while capturing stable
semantics across different views, making it robust to the input variations,
thereby reducing overfitting to self-supervision errors. The SLRNet can provide
a unified single-stage framework for various label-efficient semantic
segmentation settings: 1) WSSS with image-level labeled data, 2) SSSS with a
few pixel-level labeled data, and 3) SSSS with a few pixel-level labeled data
and many image-level labeled data. Extensive experiments on the Pascal VOC
2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both
state-of-the-art WSSS and SSSS methods with a variety of different settings,
proving its good generalizability and efficacy.

本文提出了一种自监督低秩网络 (SLRNet) 来解决有限注释下的语义分割问题，其使用了交叉视图自监督学习和集体矩阵分解来学习稳健的精确伪标签，实现了统一的单阶段框架以进行各种标签效率语义分割设置。