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

Recent advances in employing neural networks on graph domains helped push the
state of the art in link prediction tasks, particularly in recommendation
services. However, the use of temporal contextual information, often modeled as
dynamic graphs that encode the evolution of user-item relationships over time,
has been overlooked in link prediction problems. In this paper, we consider the
hypothesis that leveraging such information enables models to make better
predictions, proposing a new neural network approach for this. Our experiments,
performed on the widely used ML-100k and ML-1M datasets, show that our approach
produces better predictions in scenarios where the pattern of user-item
relationships change over time. In addition, they suggest that existing
approaches are significantly impacted by those changes.

本论文提出了一种新的神经网络方法，利用动态图表达用户 - 物品关系的演变，以更好地解决基于链接预测的推荐服务问题，实验证明在用户 - 物品关系随时间变化的场景中，该方法可以获得更好的预测结果，同时揭示了现有方法在面对这种变化时的严重影响。

动态图中的链接预测用于推荐

Link Prediction in Dynamic Graphs for Recommendation

The ubiquity of online fashion shopping demands effective recommendation
services for customers. In this paper, we study two types of fashion
recommendation: (i) suggesting an item that matches existing components in a
set to form a stylish outfit (a collection of fashion items), and (ii)
generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the
compatibility relationships among fashion items in an end-to-end fashion. More
specifically, we consider a fashion outfit to be a sequence (usually from top
to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM)
model to sequentially predict the next item conditioned on previous ones to
learn their compatibility relationships. Further, we learn a visual-semantic
space by regressing image features to their semantic representations aiming to
inject attribute and category information as a regularization for training the
LSTM. The trained network can not only perform the aforementioned
recommendations effectively but also predict the compatibility of a given
outfit. We conduct extensive experiments on our newly collected Polyvore
dataset, and the results provide strong qualitative and quantitative evidence
that our framework outperforms alternative methods.

本文提出了一种基于双向 LSTM 的视觉 - 语义嵌入方法，可以为线上时尚购物提供更有效的推荐服务，实验结果表明该方法在 Polyvore 数据集上具有优秀的性能。