Tagged articles

User Representation

6 articles · Page 1 of 1

Aug 12, 2026 · Artificial Intelligence

Live #44: Multi‑Agent Automation of Rust Code Verification & 30× Storage Reduction for User Representations

This live session reviews four KDD 2026 papers that introduce a multi‑agent framework for industrial Rust code verification, a unified quantized tokenizer that cuts user representation storage by 30×, a query‑anchored LLM approach for scenario‑adaptive user modeling, and the HIVE ensemble method for efficient out‑of‑distribution generalization, each validated with extensive experiments and real‑world deployments.

Out-of-DistributionRustUser Representation

0 likes · 9 min read

Live #44: Multi‑Agent Automation of Rust Code Verification & 30× Storage Reduction for User Representations

DataFunSummit

Jul 22, 2024 · Artificial Intelligence

From BERT to LLM: Language Model Applications in 360 Advertising Recommendation

This talk explores how 360's advertising recommendation system leverages language models—from BERT to large‑scale LLMs—to improve user interest modeling, feature extraction, and conversion‑rate prediction, detailing practical challenges, engineering solutions, experimental results, and future research directions.

AdvertisingBERTLLM

0 likes · 18 min read

From BERT to LLM: Language Model Applications in 360 Advertising Recommendation

DataFunSummit

Dec 11, 2021 · Artificial Intelligence

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

This article reviews recent advances in user representation learning for recommender systems, covering self‑supervised pre‑training, lifelong learning, multi‑task modeling, and large‑scale contrastive methods, and provides code and dataset links for key papers such as PeterRec, Conure, DUPN, ShopperBERT, PTUM, UPRec, and LURM.

PretrainingSelf-Supervised LearningUser Representation

0 likes · 11 min read

Survey of User Representation Learning and Transfer Learning in Recommendation Systems

DataFunTalk

Nov 25, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model for Recommender Systems

This talk presents a real-time attention-based look‑alike model (RALM) designed to address the long‑tail problem in recommendation systems by efficiently expanding seed users, leveraging user representation learning, attention mechanisms, and clustering to deliver timely, diverse content without retraining the model.

AttentionClusteringLong Tail

0 likes · 24 min read

Real-time Attention-based Look-alike Model for Recommender Systems

Tencent Cloud Developer

Aug 9, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model (RALM) for Recommender Systems

The Real‑time Attention‑based Look‑alike Model (RALM) converts recommendation to a user‑user problem by representing items with aggregated seed‑user embeddings, employs shared projection, local and global attention towers, and enables instant, diverse, high‑CTR recommendations without retraining, as demonstrated by its deployment in WeChat “Look‑at”.

User Representationlook-alikereal-time attention

0 likes · 13 min read

Real-time Attention-based Look-alike Model (RALM) for Recommender Systems

Alibaba Cloud Developer

Dec 19, 2018 · Artificial Intelligence

How Cross‑Domain Embedding Boosts New User Recommendations in Alibaba’s Ecosystem

This article explains the design of a Cross‑Domain Embedding (CSDE) method that transfers Alipay user features to Taobao representations, details its learning and adaptive prediction stages, and shows experimental and online results demonstrating significant conversion‑rate improvements for new and inactive users.

GaNUser Representationconversion rate prediction

0 likes · 15 min read

How Cross‑Domain Embedding Boosts New User Recommendations in Alibaba’s Ecosystem