Modern software systems are able to record vast amounts of user actions,
stored for later analysis. One of the main types of such user interaction data
is click data: the digital trace of the actions of a user through the graphical
elements of an application, website or software. While readily available, click
data is often missing a case notion: an attribute linking events from user
interactions to a specific process instance in the software. In this paper, we
propose a neural network-based technique to determine a case notion for click
data, thus enabling process mining and other process analysis techniques on
user interaction data. We describe our method, show its scalability to datasets
of large dimensions, and we validate its efficacy through a user study based on
the segmented event log resulting from interaction data of a mobility sharing
company. Interviews with domain experts in the company demonstrate that the
case notion obtained by our method can lead to actionable process insights.

该论文提出了一种基于神经网络的技术来确定点击数据的案例概念，从而使过程挖掘和其他过程分析技术能够在用户交互数据上进行。研究表明此方法可扩展到大数据集，而使用此技术可以带来可操作的流程洞察。

交互日志中的不确定案例标识符解决：用户研究

Resolving Uncertain Case Identifiers in Interaction Logs: A User Study

Among the many sources of event data available today, a prominent one is user
interaction data. User activity may be recorded during the use of an
application or website, resulting in a type of user interaction data often
called click data. An obstacle to the analysis of click data using process
mining is the lack of a case identifier in the data. In this paper, we show a
case and user study for event-case correlation on click data, in the context of
user interaction events from a mobility sharing company. To reconstruct the
case notion of the process, we apply a novel method to aggregate user
interaction data in separate user sessions-interpreted as cases-based on neural
networks. To validate our findings, we qualitatively discuss the impact of
process mining analyses on the resulting well-formed event log through
interviews with process experts.

本文提出了一种基于神经网络的方法，通过将用户交互数据聚合为单独的用户会话来解决没有案例标识符的点击数据的分析障碍，并在移动共享公司的用户交互事件的上下文中展示事件 - 案例相关性的案例和用户研究。通过与流程专家的访谈，本文进一步验证了流程挖掘分析对结果良好形成的事件日志的影响。

过程挖掘中的不确定案例标识符：基于点击数据的事件 - 案例相关性问题的用户研究

Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data

Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models.

本文提出了一种名为 DLA 的双重学习算法，该算法可以联合学习无偏好排名和无偏好倾向模型，以直接从有偏好的点击数据中学习无偏好排名模型，避免了对点击数据的特殊要求，实现了自适应学习和在线学习。实验结果表明，使用 DLA 训练的模型明显优于基于结果随机化和基于点击模型提取的相关信号的无偏好学习至排名算法。