With the increase in the business scale and number of domains in online
advertising, multi-domain ad recommendation has become a mainstream solution in
the industry. The core of multi-domain recommendation is effectively modeling
the commonalities and distinctions among domains. Existing works are dedicated
to designing model architectures for implicit multi-domain modeling while
overlooking an in-depth investigation from a more fundamental perspective of
feature distributions. This paper focuses on features with significant
differences across various domains in both distributions and effects on model
predictions. We refer to these features as domain-sensitive features, which
serve as carriers of domain distinctions and are crucial for multi-domain
modeling. Experiments demonstrate that existing multi-domain modeling methods
may neglect domain-sensitive features, indicating insufficient learning of
domain distinctions. To avoid this neglect, we propose a domain-sensitive
feature attribution method to identify features that best reflect domain
distinctions from the feature set. Further, we design a memory architecture
that extracts domain-specific information from domain-sensitive features for
the model to retrieve and integrate, thereby enhancing the awareness of domain
distinctions. Extensive offline and online experiments demonstrate the
superiority of our method in capturing domain distinctions and improving
multi-domain recommendation performance.

本研究侧重于具有显著差异的特征在多个领域中的分布和对模型预测的影响，称之为领域敏感特征，这些特征是领域区别的载体，对于多领域建模至关重要。为了避免忽视领域敏感特征，我们提出了一种能够识别最能反映领域区别的特征并从特征集中提取领域特定信息以增强建模的记忆架构。离线和在线实验证明了我们的方法在捕捉领域区别和提高多领域推荐性能方面的优越性。

多领域推荐的可检索领域敏感特征记忆

Retrievable Domain-Sensitive Feature Memory for Multi-Domain  Recommendation

We propose a simple yet a novel approach to improve completion in domain
modeling activities. Our approach exploits the power of large language models
by using few-shot prompt learning without the need to train or fine-tune those
models with large datasets that are scarce in this field. We implemented our
approach and tested it on the completion of static and dynamic domain diagrams.
Our initial evaluation shows that such an approach is effective and can be
integrated in different ways during the modeling activities.

本文提出了一种利用大型语言模型进行少样本学习的方法，以改善领域建模活动中的自动完成。该方法无需对模型进行大量数据集的训练或微调，经测试可以有效应用于静态和动态领域图示的自动完成。

利用少样本提示学习自动完成模型

Towards using Few-Shot Prompt Learning for Automating Model Completion

Service robots should be able to interact naturally with non-expert human
users, not only to help them in various tasks but also to receive guidance in
order to resolve ambiguities that might be present in the instruction. We
consider the task of visual grounding, where the agent segments an object from
a crowded scene given a natural language description. Modern holistic
approaches to visual grounding usually ignore language structure and struggle
to cover generic domains, therefore relying heavily on large datasets.
Additionally, their transfer performance in RGB-D datasets suffers due to high
visual discrepancy between the benchmark and the target domains. Modular
approaches marry learning with domain modeling and exploit the compositional
nature of language to decouple visual representation from language parsing, but
either rely on external parsers or are trained in an end-to-end fashion due to
the lack of strong supervision. In this work, we seek to tackle these
limitations by introducing a fully decoupled modular framework for
compositional visual grounding of entities, attributes, and spatial relations.
We exploit rich scene graph annotations generated in a synthetic domain and
train each module independently. Our approach is evaluated both in simulation
and in two real RGB-D scene datasets. Experimental results show that the
decoupled nature of our framework allows for easy integration with domain
adaptation approaches for Sim-To-Real visual recognition, offering a
data-efficient, robust, and interpretable solution to visual grounding in
robotic applications.

本研究介绍了一种基于模块化方法的场景感知机器人视觉定位框架，该方法通过训练每个模块来独立解析实体、属性和空间关系，并结合领域自适应技术解决常见问题。实验表明，该框架与 Sim-To-Real 实现的视觉识别方法相结合，能够提供一种数据高效、稳健且易于理解的视觉定位机器人方案。