The task of product attribute value extraction is to identify values of an attribute from product
information. Product attributes are important features, which help improve online shop** …

Learned embeddings for products are an important building block for web-scale e-
commerce recommendation systems. At Pinterest, we build a single set of product …

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs).
Despite their promising performances, they are (1) lacking proper semantic representation of …

Automatic product attribute value extraction refers to the task of identifying values of an
attribute from the product information. Product attributes are essential in improving online …

As online merchandise become more common, many studies focus on embedding-based
methods where queries and products are represented in the semantic space. These …

Product attribute value extraction involves identifying the specific values associated with
various attributes from a product profile. While existing methods often prioritize the …

Embedding-based neural retrieval is a prevalent approach to address the semantic gap
problem which often arises in product search on tail queries. In contrast, popular queries …

E-commerce authoring entails creating engaging, diverse, and targeted content to enhance
preference elicitation and retrieval experience. While Large Language Models (LLMs) have …

Previous work suggests that performance of cross-lingual information retrieval correlates
highly with the quality of Machine Translation. However, there may be a threshold beyond …

Personalizing user experience with high-quality recommendations based on user activity is
vital for e-commerce platforms. This is particularly important in scenarios where the user's …