ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

TL;DR

ESAM is a domain adaptation model that improves long-tail item ranking by aligning attribute correlations between displayed and non-displayed items, leading to better performance in sparse data scenarios.

Contribution

The paper introduces ESAM, a novel domain adaptation approach that aligns attribute correlations and employs regularization strategies to enhance long-tail item ranking without auxiliary data.

Findings

Achieves state-of-the-art results on public and industrial datasets.

Significantly improves online performance in Taobao search.

Effectively addresses data sparsity and cold start issues.

Abstract

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items). Due to the sample selection bias, the long-tail items lack sufficient records to learn good feature representations, i.e. data sparsity and cold start problems. The resultant distribution discrepancy between displayed and non-displayed items would cause poor long-tail performance. To this end, we propose an entire space adaptation model (ESAM) to address this problem from the perspective of domain adaptation (DA). ESAM regards displayed and non-displayed items as source and target domains respectively. Specifically, we design the attribute correlation alignment that considers the correlation between high-level attributes of the item to achieve distribution alignment. Furthermore, we introduce two effective regularization strategies, i.e. \textit{center-wise clustering} and \textit{self-training} to improve DA process. Without requiring any auxiliary information and auxiliary domains, ESAM transfers the knowledge from displayed items to non-displayed items for alleviating the distribution inconsistency. Experiments on two public datasets and a large-scale industrial dataset collected from Taobao demonstrate that ESAM achieves state-of-the-art performance, especially in the long-tail space. Besides, we deploy ESAM to the Taobao search engine, leading to significant improvement on online performance. The code is available at \url{https://github.com/A-bone1/ESAM.git}