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User Embedding

3 articles · Page 1 of 1

Jun 20, 2024 · Artificial Intelligence

How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper

This article analyzes Kuaishou’s May 2024 paper on LLM‑driven recommendation, detailing its dual‑tower architecture, contrastive learning of user and item embeddings, and a CVR‑auxiliary task that together improve cold‑start handling and boost both offline and online AUC metrics.

Industrial ApplicationItem EmbeddingLLM

0 likes · 10 min read

How LLMs Transform Recommendation Systems: Insights from Kuaishou’s LERAN Paper

NetEase Cloud Music Tech Team

Mar 31, 2022 · Industry Insights

How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music

This article details NetEase Cloud Music's technical approach to building implicit user relationship chains—using SimHash, Item2Vec, and MetaPath2Vec embeddings, large‑scale vector search, and a unified service architecture—to address cold‑start challenges across multiple business scenarios.

Item2VecMetaPath2VecSimHash

0 likes · 20 min read

How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music

DataFunTalk

Jan 23, 2022 · Artificial Intelligence

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

This article presents a systematic study of recall techniques for new‑user cold‑start in content recommendation, describing a baseline two‑tower model, a Dual Attention Network (DAN) fusion approach, and an enhanced Contextual‑Gate DAN that dynamically balances content and product sequences, together with offline and online evaluation results and future directions.

Deep LearningUser Embeddingcold-start

0 likes · 12 min read

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation