We present a new task setting for attribute mining on e-commerce products,
serving as a practical solution to extract open-world attributes without
extensive human intervention. Our supervision comes from a high-quality seed
attribute set bootstrapped from existing resources, and we aim to expand the
attribute vocabulary of existing seed types, and also to discover any new
attribute types automatically. A new dataset is created to support our setting,
and our approach Amacer is proposed specifically to tackle the limited
supervision. Especially, given that no direct supervision is available for
those unseen new attributes, our novel formulation exploits self-supervised
heuristic and unsupervised latent attributes, which attains implicit semantic
signals as additional supervision by leveraging product context. Experiments
suggest that our approach surpasses various baselines by 12 F1, expanding
attributes of existing types significantly by up to 12 times, and discovering
values from 39% new types.

提出了一种新的任务设置，用于在电子商务产品中进行属性挖掘，通过使用高质量种子属性集进行监督学习，结合自监督启发式和无监督的潜在属性，利用产品上下文作为附加监督，自动扩展属性词汇，发现新的属性类型。

面向开放世界的产品属性挖掘：一种轻度监督的方法

Towards Open-World Product Attribute Mining: A Lightly-Supervised  Approach

Collecting fine-grained labels usually requires expert-level domain knowledge
and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data
augmentation strategy at attribute level to expand the fine-grained samples.
The principle lies in that attribute features are shared among fine-grained
sub-categories, and can be seamlessly transferred among images. Toward this
goal, we propose an automatic attribute mining approach to discover attributes
that belong to the same super-category, and Attribute Mix is operated by mixing
semantically meaningful attribute features from two images. Attribute Mix is a
simple but effective data augmentation strategy that can significantly improve
the recognition performance without increasing the inference budgets.
Furthermore, since attributes can be shared among images from the same
super-category, we further enrich the training samples with attribute level
labels using images from the generic domain. Experiments on widely used
fine-grained benchmarks demonstrate the effectiveness of our proposed method.

本文提出了一种数据增强策略 Attribute Mix，通过属性级别的数据增强方法，利用自动属性挖掘技术扩展细粒度样本，从而显著提高图像识别性能。同时基于属性的共享特点，在广义域的图像上训练可扩展属性级别的标签。实验证明了该方法的有效性。

属性混合：用于细粒度识别的语义数据增强

Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition

To alleviate sparsity and cold start problem of collaborative filtering based
recommender systems, researchers and engineers usually collect attributes of
users and items, and design delicate algorithms to exploit these additional
information. In general, the attributes are not isolated but connected with
each other, which forms a knowledge graph (KG). In this paper, we propose
Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that
captures inter-item relatedness effectively by mining their associated
attributes on the KG. To automatically discover both high-order structure
information and semantic information of the KG, we sample from the neighbors
for each entity in the KG as their receptive field, then combine neighborhood
information with bias when calculating the representation of a given entity.
The receptive field can be extended to multiple hops away to model high-order
proximity information and capture users' potential long-distance interests.
Moreover, we implement the proposed KGCN in a minibatch fashion, which enables
our model to operate on large datasets and KGs. We apply the proposed model to
three datasets about movie, book, and music recommendation, and experiment
results demonstrate that our approach outperforms strong recommender baselines.

本文提出了 Knowledge Graph Convolutional Networks (KGCN) 的方法，通过在知识图谱上挖掘和利用互相关联的属性来有效地捕捉物品之间的相关性，扩展感受野以建模高阶邻近信息，并将邻居信息与偏差相结合计算给定实体的表示，以优化协同过滤型推荐系统的性能。实验结果表明，该方法在电影、图书和音乐推荐的三个数据集上优于强基线算法。