This paper deals with developing techniques for the reconstruction of
high-dimensional datasets given each bivariate projection, as would be found in
a matrix scatterplot. A graph-based solution is introduced, involving
clique-finding, providing a set of possible rows that might make up the
original dataset. Complications are discussed, including cases where phantom
cliques are found, as well as cases where an exact solution is impossible.
Additional methods are shown, with some dealing with fully deducing rows and
others dealing with having to creatively produce methods that find some
possibilities to be more likely than others. Results show that these methods
are highly successful in recreating a significant portion of the original
dataset in many cases - for randomly generated and real-world datasets - with
the factors leading to a greater rate of failure being lower dimension, higher
n, and lower interval.

通过图形化解决方案重新构建高维数据集，包括查找团伙和讨论复杂情况，再加以其他方法处理，研究结果表明这些方法在很多情况下能够成功重建原始数据集。

从双变量投影重构高维数据集

Reconstructing High-Dimensional Datasets From Their Bivariate  Projections

Dream11 takes pride in being a unique platform that enables over 190 million
fantasy sports users to demonstrate their skills and connect deeper with their
favorite sports. While managing such a scale, one issue we are faced with is
duplicate/multiple account creation in the system. This is done by some users
with the intent of abusing the platform, typically for bonus offers. The
challenge is to detect these multiple accounts before it is too late. We
propose a graph-based solution to solve this problem in which we first predict
edges/associations between users. Using the edge information we highlight
clusters of colluding multiple accounts. In this paper, we talk about our
distributed ML system which is deployed to serve and support the inferences
from our detection models. The challenge is to do this in real-time in order to
take corrective actions. A core part of this setup also involves
human-in-the-loop components for validation, feedback, and ground-truth
labeling.

Dream11 采用基于图的解决方案来检测平台上的重复账户，并且开发了分布式机器学习系统以实时支持推断和采取纠正措施，其中也包括人工验证和标记。

FENCE：用于规模化幻想体育比赛的实时多重 ID 检测的公平保障网络链实体

FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID  Detection at Scale In Fantasy Sports

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects.

本文提出了一种名为 TiDA-GCN 的图形解决方案，以解决跨域顺序推荐中的挑战，包括多个实体之间的关系的捕获、明确的跨域图结构的建立和时间间隔信息的利用，通过域感知图卷积网络和有效的注意机制实现用户特定节点表示的学习，以及引入账户感知自注意模块进行交互特征的学习。实验结果表明，该方法在多个方面表现出优越性。

共享账户跨域序列推荐的时间间隔增强图神经网络

Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of
recommending the next item based on a sequence of recorded user behaviors,
where multiple users share a single account, and their behaviours are available
in multiple domains. Existing work on solving SCSR mainly relies on mining
sequential patterns via RNN-based models, which are not expressive enough to
capture the relationships among multiple entities. Moreover, all existing
algorithms try to bridge two domains via knowledge transfer in the latent
space, and the explicit cross-domain graph structure is unexploited. In this
work, we propose a novel graph-based solution, namely DA-GCN, to address the
above challenges. Specifically, we first link users and items in each domain as
a graph. Then, we devise a domain-aware graph convolution network to learn
user-specific node representations. To fully account for users' domain-specific
preferences on items, two novel attention mechanisms are further developed to
selectively guide the message passing process. Extensive experiments on two
real-world datasets are conducted to demonstrate the superiority of our DA-GCN
method.

本论文提出了一种基于图的推荐算法 DA-GCN，通过关注用户特定节点表示和选定注意机制来解决交叉领域序列推荐任务中的挑战。