Product matching, the task of identifying different representations of the
same product for better discoverability, curation, and pricing, is a key
capability for online marketplace and e-commerce companies. We present a robust
multi-modal product matching system in an industry setting, where large
datasets, data distribution shifts and unseen domains pose challenges. We
compare different approaches and conclude that a relatively straightforward
projection of pretrained image and text encoders, trained through contrastive
learning, yields state-of-the-art results, while balancing cost and
performance. Our solution outperforms single modality matching systems and
large pretrained models, such as CLIP. Furthermore we show how a
human-in-the-loop process can be combined with model-based predictions to
achieve near perfect precision in a production system.

在线市场与电子商务公司中，产品匹配是识别同一产品不同表示以提高可发现性、整理性和定价性的重要能力。我们在一个行业环境中提出了一个强大的多模态产品匹配系统，其中大规模的数据集、数据分布转移和未知领域带来了挑战。我们比较了不同的方法，并得出结论，通过预先训练的图像和文本编码器的相对简单的投影，通过对比学习进行训练，可以在成本和性能方面取得最新的结果。我们的解决方案优于单模态匹配系统和大规模预训练模型，例如 CLIP。此外，我们展示了如何将人机协作过程与基于模型的预测相结合，实现在生产系统中接近完美的精度。

时尚电商中的多模态端到端产品匹配

End-to-end multi-modal product matching in fashion e-commerce

We present a framework that allows users to incorporate the semantics of
their domain knowledge for topic model refinement while remaining
model-agnostic. Our approach enables users to (1) understand the semantic space
of the model, (2) identify regions of potential conflicts and problems, and (3)
readjust the semantic relation of concepts based on their understanding,
directly influencing the topic modeling. These tasks are supported by an
interactive visual analytics workspace that uses word-embedding projections to
define concept regions which can then be refined. The user-refined concepts are
independent of a particular document collection and can be transferred to
related corpora. All user interactions within the concept space directly affect
the semantic relations of the underlying vector space model, which, in turn,
change the topic modeling. In addition to direct manipulation, our system
guides the users' decision-making process through recommended interactions that
point out potential improvements. This targeted refinement aims at minimizing
the feedback required for an efficient human-in-the-loop process. We confirm
the improvements achieved through our approach in two user studies that show
topic model quality improvements through our visual knowledge externalization
and learning process.

提供了一个框架，允许用户在保持模型无关性的同时，将其领域知识的语义纳入主题模型的完善中。使用交互式视觉分析工作区支持用户完成语义空间的理解、潜在冲突和问题区域的识别以及基于对概念理解的重新调整，从而直接影响主题建模。通过操作与推荐交互相结合，目标完善旨在最小化高效的人机协同交互迭代过程所需的反馈次数，两项用户研究证实了该方法提高了主题模型质量。