This paper delves into the contrasting roles of data within academic and
industrial spheres, highlighting the divergence between Data-Centric AI and
Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on
the primacy of high-quality data for model performance, Model-Agnostic AI
prioritizes algorithmic flexibility, often at the expense of data quality
considerations. This distinction reveals that academic standards for data
quality frequently do not meet the rigorous demands of industrial applications,
leading to potential pitfalls in deploying academic models in real-world
settings. Through a comprehensive analysis, we address these disparities,
presenting both the challenges they pose and strategies for bridging the gap.
Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which
aims to reconcile these differences by integrating model considerations into
data optimization processes. This approach underscores the necessity for
evolving data requirements that are sensitive to the nuances of both academic
research and industrial deployment. By exploring these discrepancies, we aim to
foster a more nuanced understanding of data's role in AI development and
encourage a convergence of academic and industrial standards to enhance AI's
real-world applicability.

本研究探讨了学术和工业领域中数据的对比角色，突出了数据中心化人工智能和模型不可知人工智能方法之间的差异。我们认为，数据中心化人工智能注重高质量数据对模型性能的重要性，而模型不可知人工智能则更注重算法的灵活性，往往忽视了数据质量的考虑。通过全面分析，我们解决了这些差异带来的挑战，并提出了弥合这一差距的策略。此外，我们提出了一种新的范式：基于模型的数据中心化人工智能，旨在将模型因素纳入数据优化过程中。这种方法强调需要发展对学术研究和工业部署细微差别敏感的数据需求。通过探索这些差异，我们旨在培养对数据在人工智能发展中作用的更加细致的理解，并鼓励学术和工业标准的融合，以增强人工智能在现实世界中的适用性。

基于模型的以数据为中心的人工智能：学术理想与工业实用之间的桥梁

Model-Based Data-Centric AI: Bridging the Divide Between Academic Ideals  and Industrial Pragmatism

Cross-domain recommendation (CDR) is an important method to improve
recommender system performance, especially when observations in target domains
are sparse. However, most existing cross-domain recommendations fail to fully
utilize the target domain's special features and are hard to be generalized to
new domains. The designed network is complex and is not suitable for rapid
industrial deployment. Our method introduces a two-step domain-aware
cross-attention, extracting transferable features of the source domain from
different granularity, which allows the efficient expression of both domain and
user interests. In addition, we simplify the training process, and our model
can be easily deployed on new domains. We conduct experiments on both public
datasets and industrial datasets, and the experimental results demonstrate the
effectiveness of our method. We have also deployed the model in an online
advertising system and observed significant improvements in both
Click-Through-Rate (CTR) and effective cost per mille (ECPM).

我们的研究提出了一种两步领域感知的交叉关注方法，能够从源领域的不同粒度中提取可迁移的特征，有效表达领域和用户兴趣，简化了训练流程，适合于新领域的快速部署，并通过实验证明了方法的有效性，还在在线广告系统中部署了模型，观察到了单击率和有效千次展示费用的显著改善。