Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores
Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.
Code (0)
등록된 구현이 없습니다.
Tasks
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSimilar Papers 제목 키워드 기반
MAGIC: Mining an Augmented Graph using INK, starting from a CSV
A large portion of structured data does not yet reap the benefits of the Semantic Web. Therefore, The “Tabular Data to Knowledge Graph Matching” competition at ISWC tries to bridge this gap by evaluating and promoting th…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingTable annotationCan Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtai…
Deep LearningOn efficiency gains via augmenting a tiny sample with a massive auxiliary sample
In this paper, we study the problem of augmenting a tiny target sample with a massive auxiliary sample. Utilizing Tukey's factorization, there are two popular approaches: the inverse probability weight (IPW) and the full…
Searching from Area to Point: A Hierarchical Framework for Semantic-Geometric Combined Feature Matching
Feature matching is a crucial technique in computer vision. A unified perspective for this task is to treat it as a searching problem, aiming at an efficient search strategy to narrow the search space to point matches be…
Pose EstimationFeatAug-DETR: Enriching One-to-Many Matching for DETRs with Feature Augmentation
One-to-one matching is a crucial design in DETR-like object detection frameworks. It enables the DETR to perform end-to-end detection. However, it also faces challenges of lacking positive sample supervision and slow con…
Data Augmentationobject-detectionObject Detection