This work explores the problem of generating task graphs of real-world
activities. Different from prior formulations, we consider a setting where text
transcripts of instructional videos performing a real-world activity (e.g.,
making coffee) are provided and the goal is to identify the key steps relevant
to the task as well as the dependency relationship between these key steps. We
propose a novel task graph generation approach that combines the reasoning
capabilities of instruction-tuned language models along with clustering and
ranking components to generate accurate task graphs in a completely
unsupervised manner. We show that the proposed approach generates more accurate
task graphs compared to a supervised learning approach on tasks from the ProceL
and CrossTask datasets.

本文探讨了生成真实世界活动任务图的问题，提出了一种无监督的任务图生成方法，结合语言模型的推理能力、聚类和排名组件，比 ProceL 和 CrossTask 数据集中的有监督学习方法生成更准确的任务图。

从教学视频转录中生成无监督任务图

Unsupervised Task Graph Generation from Instructional Video Transcripts

Different from the traditional recommender system, the session-based
recommender system introduces the concept of the session, i.e., a sequence of
interactions between a user and multiple items within a period, to preserve the
user's recent interest. The existing work on the session-based recommender
system mainly relies on mining sequential patterns within individual sessions,
which are not expressive enough to capture more complicated dependency
relationships among items. In addition, it does not consider the cross-session
information due to the anonymity of the session data, where the linkage between
different sessions is prevented. In this paper, we solve these problems with
the graph neural networks technique. First, each session is represented as a
graph rather than a linear sequence structure, based on which a novel Full
Graph Neural Network (FGNN) is proposed to learn complicated item dependency.
To exploit and incorporate cross-session information in the individual
session's representation learning, we further construct a Broadly Connected
Session (BCS) graph to link different sessions and a novel Mask-Readout
function to improve session embedding based on the BCS graph. Extensive
experiments have been conducted on two e-commerce benchmark datasets, i.e.,
Yoochoose and Diginetica, and the experimental results demonstrate the
superiority of our proposal through comparisons with state-of-the-art
session-based recommender models.

本文提出了一种基于图神经网络 (Full Graph Neural Network, FGNN) 以及广联跨时加工 (Broadly Connected Session, BCS) 的新型会话 (session) 推荐算法，旨在更好地捕捉复杂的物品依赖关系和跨时信息。在 Yoochoose 和 Diginetica 等两个电子商务基准数据集上广泛测试，实验结果表明，与现有会话推荐模型相比，该算法具有优异的性能。

基于图神经网络的跨会话信息利用

Exploiting Cross-Session Information for Session-based Recommendation  with Graph Neural Networks

Asynchronous events on the continuous time domain, e.g., social media actions
and stock transactions, occur frequently in the world. The ability to recognize
occurrence patterns of event sequences is crucial to predict which typeof
events will happen next and when. A de facto standard mathematical framework to
do this is the Hawkes process. In order to enhance expressivity of multivariate
Hawkes processes, conventional statistical methods and deep recurrent networks
have been employed to modify its intensity function. The former is highly
interpretable and requires small size of training data but relies on correct
model design while the latter has less dependency on prior knowledge and is
more powerful in capturing complicated patterns. We leverage pros and cons of
these models and propose a self-attentive Hawkes process(SAHP). The proposed
method adapts self-attention to fit the intensity function of Hawkes processes.
This design has two benefits:(1) compared with conventional statistical
methods, the SAHP is more powerful to identify complicated dependency
relationships between temporal events; (2)compared with deep recurrent
networks, the self-attention mechanism is able to capture longer historical
information, and is more interpretable because the learnt attention weight
tensor shows contributions of each historical event. Experiments on four
real-world datasets demonstrate the effectiveness of the proposed method.

提出了一种利用 self-attention 机制进行 intensity function 拟合的 self-attentive Hawkes process 方法，相较于传统的统计方法和深度循环神经网络，该方法能更好地识别时间事件之间的复杂依赖关系，并且能够捕捉更长的历史信息，可以针对多变量事件序列进行有效的复杂模式预测。