Learning and Transferring IDs Representation in E-commerce
Kui Zhao, Yuechuan Li, Zhaoqian Shuai, Cheng Yang

TL;DR
This paper introduces an embedding framework for representing various IDs in e-commerce, improving upon traditional methods by capturing relationships and enabling transfer across items, domains, and tasks.
Contribution
It proposes a novel embedding-based approach to learn and transfer ID representations, addressing sparsity and relationship modeling issues in e-commerce data.
Findings
Effective in measuring item similarity
Enables transfer to unseen items
Validated on Hema App with positive results
Abstract
Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc. The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones. In this paper, we propose an embedding based framework to learn and transfer the representation of IDs. As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions. By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space. Subsequently, the learned representations are utilized…
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