A sequential pattern with negation, or negative sequential pattern, takes the
form of a sequential pattern for which the negation symbol may be used in front
of some of the pattern's itemsets. Intuitively, such a pattern occurs in a
sequence if negated itemsets are absent in the sequence. Recent work has shown
that different semantics can be attributed to these pattern forms, and that
state-of-the-art algorithms do not extract the same sets of patterns. This
raises the important question of the interpretability of sequential pattern
with negation. In this study, our focus is on exploring how potential users
perceive negation in sequential patterns. Our aim is to determine whether
specific semantics are more "intuitive" than others and whether these align
with the semantics employed by one or more state-of-the-art algorithms. To
achieve this, we designed a questionnaire to reveal the semantics' intuition of
each user. This article presents both the design of the questionnaire and an
in-depth analysis of the 124 responses obtained. The outcomes indicate that two
of the semantics are predominantly intuitive; however, neither of them aligns
with the semantics of the primary state-of-the-art algorithms. As a result, we
provide recommendations to account for this disparity in the conclusions drawn.

该研究探讨了在顺序模式中使用否定符号的语义解释的直观性，并分析了用户对不同语义的认知。结果表明，两种语义较为直观，但与现有先进算法的语义并不一致，因此提出了对这种差异进行考虑的建议。

关于具有否定语义的序列模式的调查

A survey on the semantics of sequential patterns with negation

Estimating post-click conversion rate (CVR) accurately is crucial for ranking
systems in industrial applications such as recommendation and advertising.
Conventional CVR modeling applies popular deep learning methods and achieves
state-of-the-art performance. However it encounters several task-specific
problems in practice, making CVR modeling challenging. For example,
conventional CVR models are trained with samples of clicked impressions while
utilized to make inference on the entire space with samples of all impressions.
This causes a sample selection bias problem. Besides, there exists an extreme
data sparsity problem, making the model fitting rather difficult. In this
paper, we model CVR in a brand-new perspective by making good use of sequential
pattern of user actions, i.e., impression -> click -> conversion. The proposed
Entire Space Multi-task Model (ESMM) can eliminate the two problems
simultaneously by i) modeling CVR directly over the entire space, ii) employing
a feature representation transfer learning strategy. Experiments on dataset
gathered from Taobao's recommender system demonstrate that ESMM significantly
outperforms competitive methods. We also release a sampling version of this
dataset to enable future research. To the best of our knowledge, this is the
first public dataset which contains samples with sequential dependence of click
and conversion labels for CVR modeling.

该论文提出了一种基于用户行为先后顺序的 CTR 建模方法，即评估用户广告点击数据的转化率，并使用全空间多任务模型 (ESMM) 进行直接建模，该方法可以同时解决采样偏差问题和数据稀疏性问题，并在淘宝推荐系统数据集上进行了实验证明其比竞争方法具有更好的性能。同时，文中还发布了包含点击和转化标签的序列依赖采样版本，成为首个公开的采样版本数据集。