Recently, industrial recommendation services have been boosted by the
continual upgrade of deep learning methods. However, they still face de-biasing
challenges such as exposure bias and cold-start problem, where circulations of
machine learning training on human interaction history leads algorithms to
repeatedly suggest exposed items while ignoring less-active ones. Additional
problems exist in multi-scenario platforms, e.g. appropriate data fusion from
subsidiary scenarios, which we observe could be alleviated through graph
structured data integration via message passing.
In this paper, we present a multi-graph structured multi-scenario
recommendation solution, which encapsulates interaction data across scenarios
with multi-graph and obtains representation via graph learning. Extensive
offline and online experiments on real-world datasets are conducted where the
proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video
Views per capita on new users over deployed set of baselines and outperforms
regular method in increasing the number of outer-scenario videos by 25% and
video watches by 116%, validating its superiority in activating cold videos and
enriching target recommendation.

本文提出了一种多图结构的多场景推荐解决方案，通过图学习来在各场景之间交互数据并获得表示，从而使其能够在激活冷门视频和丰富目标推荐方面表现出卓越性。

大规模在线视频服务中基于多图的多场景推荐

Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services

Learning effective embedding has been proved to be useful in many real-world
problems, such as recommender systems, search ranking and online advertisement.
However, one of the challenges is data sparsity in learning large-scale item
embedding, as users' historical behavior data are usually lacking or
insufficient in an individual domain. In fact, user's behaviors from different
domains regarding the same items are usually relevant. Therefore, we can learn
complete user behaviors to alleviate the sparsity using complementary
information from correlated domains. It is intuitive to model users' behaviors
using graph, and graph neural networks (GNNs) have recently shown the great
power for representation learning, which can be used to learn item embedding.
However, it is challenging to transfer the information across domains and learn
cross-domain representation using the existing GNNs. To address these
challenges, in this paper, we propose a novel model - Deep Multi-Graph
Embedding (DMGE) to learn cross-domain representation. Specifically, we first
construct a multi-graph based on users' behaviors from different domains, and
then propose a multi-graph neural network to learn cross-domain representation
in an unsupervised manner. Particularly, we present a multiple-gradient descent
optimizer for efficiently training the model. We evaluate our approach on
various large-scale real-world datasets, and the experimental results show that
DMGE outperforms other state-of-art embedding methods in various tasks.

本文提出了一种名为 Deep Multi-Graph Embedding (DMGE) 的新颖模型，它使用图神经网络和多源数据学习，以适应大规模项目内嵌物的数据稀疏性问题和领域间信息迁移问题。实验结果表明，DMGE 比其他现有的嵌入方法在各种任务中表现更好。

多图神经网络学习跨领域表示

Learning Cross-Domain Representation with Multi-Graph Neural Network

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and
establish a novel and theoretically-supported paradigm for large-scale graph
analysis. The proposed method is based on the fact that Gromov-Wasserstein
discrepancy is a pseudometric on graphs. Given two graphs, the optimal
transport associated with their Gromov-Wasserstein discrepancy provides the
correspondence between their nodes and achieves graph matching. When one of the
graphs has isolated but self-connected nodes ($i.e.$, a disconnected graph),
the optimal transport indicates the clustering structure of the other graph and
achieves graph partitioning. Using this concept, we extend our method to
multi-graph partitioning and matching by learning a Gromov-Wasserstein
barycenter graph for multiple observed graphs; the barycenter graph plays the
role of the disconnected graph, and since it is learned, so is the clustering.
Our method combines a recursive $K$-partition mechanism with a regularized
proximal gradient algorithm, whose time complexity is $\mathcal{O}(K(E+V)\log_K
V)$ for graphs with $V$ nodes and $E$ edges. To our knowledge, our method is
the first attempt to make Gromov-Wasserstein discrepancy applicable to
large-scale graph analysis and unify graph partitioning and matching into the
same framework. It outperforms state-of-the-art graph partitioning and matching
methods, achieving a trade-off between accuracy and efficiency.

提出了一个可扩展的 Gromov-Wasserstein 学习（S-GWL）方法，用于大规模图分析，通过学习多个观察到的图的 Gromov-Wasserstein 重心图来实现多图分区和匹配，并将其统一到同一框架中，从而在准确性和效率之间取得了平衡。