Temporal interaction graphs (TIGs), consisting of sequences of timestamped
interaction events, are prevalent in fields like e-commerce and social
networks. To better learn dynamic node embeddings that vary over time,
researchers have proposed a series of temporal graph neural networks for TIGs.
However, due to the entangled temporal and structural dependencies, existing
methods have to process the sequence of events chronologically and
consecutively to ensure node representations are up-to-date. This prevents
existing models from parallelization and reduces their flexibility in
industrial applications. To tackle the above challenge, in this paper, we
propose TIGER, a TIG embedding model that can restart at any timestamp. We
introduce a restarter module that generates surrogate representations acting as
the warm initialization of node representations. By restarting from multiple
timestamps simultaneously, we divide the sequence into multiple chunks and
naturally enable the parallelization of the model. Moreover, in contrast to
previous models that utilize a single memory unit, we introduce a dual memory
module to better exploit neighborhood information and alleviate the staleness
problem. Extensive experiments on four public datasets and one industrial
dataset are conducted, and the results verify both the effectiveness and the
efficiency of our work.

该研究提出了一种名为 TIGER 的时序图嵌入模型，引入了一个重启模块来生成代理表示，使模型能够从多个时间戳同时重启，从而使模型能够并行。与之前的模型不同，该模型引入了双重记忆模块以更好地利用邻域信息并减轻旧问题。

TIGER: 带重启的时间交互图嵌入

TIGER: Temporal Interaction Graph Embedding with Restarts

This paper presents a new metric called TIGEr for the automatic evaluation of
image captioning systems. Popular metrics, such as BLEU and CIDEr, are based
solely on text matching between reference captions and machine-generated
captions, potentially leading to biased evaluations because references may not
fully cover the image content and natural language is inherently ambiguous.
Building upon a machine-learned text-image grounding model, TIGEr allows to
evaluate caption quality not only based on how well a caption represents image
content, but also on how well machine-generated captions match human-generated
captions. Our empirical tests show that TIGEr has a higher consistency with
human judgments than alternative existing metrics. We also comprehensively
assess the metric's effectiveness in caption evaluation by measuring the
correlation between human judgments and metric scores.

本论文提出了一种名为 TIGEr 的新度量标准，用于自动评估图像标题生成系统，该度量标准不仅基于标题对图像内容的表现，还基于机器生成的标题与人生成的标题匹配程度；经实验测试，TIGEr 与人类判断的一致性要比其他度量标准更高，同时也通过人员评分与度量分数的相关性全面评估了该度量标准的有效性。