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NewBeeNLP
NewBeeNLP
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
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.

Item2VecMetaPath2VecRecommendation Systems
0 likes · 20 min read
How Implicit Relationship Chains Solve Cold‑Start Problems at NetEase Cloud Music
DataFunTalk
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.

RecommendationUser Embeddingcold-start
0 likes · 12 min read
Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation