User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

This article presents Alibaba's comprehensive approach to user modeling for search CTR/CVR ranking, detailing the abstraction of user information, multi‑scale behavior processing, enhanced transformer‑based model structures, client‑side click and exposure modeling, and experimental results showing significant AUC improvements.

AlibabaAttention MechanismCTR prediction

0 likes · 18 min read

User Modeling for Search Ranking: Practices, Model Design, and Experimental Analysis at Alibaba

Alibaba Cloud Developer

Jan 7, 2020 · Artificial Intelligence

How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention

This article details Alibaba’s Taobao search CTR/CVR user modeling approach, covering background, model architecture with self‑attention and attention pooling, handling short‑term, long‑term, and on‑device behavior sequences, experimental results showing AUC improvements, and future directions.

CTR predictionSelf-Attentionbehavior sequence

0 likes · 20 min read

How Alibaba Boosts Search Relevance with Advanced User Modeling and Self‑Attention

Alibaba Cloud Developer

Aug 15, 2019 · Artificial Intelligence

How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training

This article introduces Auto Risk, a deep‑learning risk model for behavior‑sequence data that leverages unsupervised pre‑training with proxy tasks, details its convolution‑attention encoder, demonstrates significant gains across multiple business scenarios, and highlights its strong small‑sample and analogy capabilities.

Deep LearningRisk ModelingUnsupervised Learning

0 likes · 20 min read

How Auto Risk Transforms Behavior Sequence Data with Unsupervised Pre‑Training

Alibaba Cloud Developer

Jul 30, 2019 · Artificial Intelligence

Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences

This article introduces Auto Risk, a behavior‑sequence deep‑learning framework that uses unsupervised pre‑training with proxy tasks to learn universal feature representations from massive unlabeled data, achieving significant gains in risk‑control scenarios, improving AUC, supporting multi‑scene generalization and small‑sample learning.

Deep LearningRisk ModelingUnsupervised Learning

0 likes · 20 min read

Auto Risk: Pretraining Deep Models on Unlabeled Behavior Sequences

AntTech

Sep 7, 2018 · Artificial Intelligence

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

This article explains how Alipay combats the surge of mobile payment fraud by upgrading its risk‑identification system with deep‑learning techniques, modeling victim and fraudster behavior sequences using LSTM, and integrating the resulting scores into existing models to achieve a measurable increase in detection coverage.

Deep LearningLSTMRisk Modeling

0 likes · 11 min read

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection