We introduce SAGE; a Generative LLM for inferring attribute values for
products across world-wide e-Commerce catalogs. We introduce a novel
formulation of the attribute-value prediction problem as a Seq2Seq
summarization task, across languages, product types and target attributes. Our
novel modeling approach lifts the restriction of predicting attribute values
within a pre-specified set of choices, as well as, the requirement that the
sought attribute values need to be explicitly mentioned in the text. SAGE can
infer attribute values even when such values are mentioned implicitly using
periphrastic language, or not-at-all-as is the case for common-sense defaults.
Additionally, SAGE is capable of predicting whether an attribute is
inapplicable for the product at hand, or non-obtainable from the available
information. SAGE is the first method able to tackle all aspects of the
attribute-value-prediction task as they arise in practical settings in
e-Commerce catalogs. A comprehensive set of experiments demonstrates the
effectiveness of the proposed approach, as well as, its superiority against
state-of-the-art competing alternatives. Moreover, our experiments highlight
SAGE's ability to tackle the task of predicting attribute values in zero-shot
setting; thereby, opening up opportunities for significantly reducing the
overall number of labeled examples required for training.

我们介绍了 SAGE，这是一个用于在全球电子商务目录中推断产品属性值的生成型 LLM。我们引入了一种新颖的属性值预测问题的公式化，作为一个跨语言、产品类型和目标属性的 Seq2Seq 总结任务。我们的新颖建模方法消除了在预测属性值时必须从预定选择集合中进行选择的限制，以及必须在文本中明确提及所需属性值的要求。SAGE 甚至可以推断那些用迂回语言含蓄提及或根本未提及的属性值，例如普遍常识默认值。此外，SAGE 可以预测某个属性在手头产品中是否不适用或无法从可用信息中获得。SAGE 是首个能够应对电子商务目录中实际设置中出现的属性 - 值预测任务的方法。一系列综合实验证明了所提方法的效果以及其对现有最先进方法的优越性。此外，我们的实验突出了 SAGE 在零样本情境中预测属性值的能力，从而为大幅减少所需的标记示例总数提供了机会。

SAGE：面向十亿级产品目录的结构化属性值生成

SAGE: Structured Attribute Value Generation for Billion-Scale Product  Catalogs

In this paper, we revisit the problem of product item classification for
large-scale e-commerce catalogs. The taxonomy of e-commerce catalogs consists
of thousands of genres to which are assigned items that are uploaded by
merchants on a continuous basis. The genre assignments by merchants are often
wrong but treated as ground truth labels in automatically generated training
sets, thus creating a feedback loop that leads to poorer model quality over
time. This problem of taxonomy classification becomes highly pronounced due to
the unavailability of sizable curated training sets.
Under such a scenario it is common to combine multiple classifiers to combat
poor generalization performance from a single classifier. We propose an
extensible deep learning based classification model framework that benefits
from the simplicity and robustness of averaging ensembles and fusion based
classifiers. We are also able to use metadata features and low-level feature
engineering to boost classification performance. We show these improvements
against robust industry standard baseline models that employ hyperparameter
optimization.
Additionally, due to continuous insertion, deletion and updates to real-world
high-volume e-commerce catalogs, assessing model performance for deployment
using A/B testing and/or manual annotation becomes a bottleneck. To this end,
we also propose a novel way to evaluate model performance using user sessions
that provides better insights in addition to traditional measures of precision
and recall.

本研究回顾了针对大规模电子商务目录的产品项分类问题。我们提出了一种基于深度学习的分类模型框架，利用多个分类器的组合和融合来改善分类性能，并使用元数据特征和低级特征工程来提升模型质量。此外，我们还提出了一种使用用户会话评估模型性能的新方法，相较于传统的精确度和召回率等评估指标，提供更详尽的洞察力。