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JD Cloud Developers
JD Cloud Developers
May 28, 2025 · Artificial Intelligence

Uncovering the ‘Sandwich’ Bottleneck in Residual Quantized Semantic IDs for Generative Search

This study investigates the “sandwich” bottleneck observed in residual‑quantized semantic identifiers (RQ‑SID) used in generative search and recommendation systems, revealing that token concentration in intermediate codebooks caused by path sparsity and long‑tail distributions degrades performance, and proposes two effective mitigation strategies that improve efficiency and generalization in e‑commerce applications.

e-commerce recommendationgenerative searchlong-tail distribution
0 likes · 13 min read
Uncovering the ‘Sandwich’ Bottleneck in Residual Quantized Semantic IDs for Generative Search
DataFunTalk
DataFunTalk
Jan 23, 2023 · Databases

KGraph: Architecture, Performance, and Applications of Kuaishou's In‑House Graph Platform

This article introduces KGraph, Kuaishou's self‑developed graph platform, detailing its directed heterogeneous property‑graph model, distributed KV storage with PMem persistence, high‑performance RPC framework, key challenges it solves, benchmark results, real‑time recommendation use cases, and future development directions.

KGraphdistributed storagee-commerce recommendation
0 likes · 16 min read
KGraph: Architecture, Performance, and Applications of Kuaishou's In‑House Graph Platform
JD Retail Technology
JD Retail Technology
Jun 27, 2022 · Artificial Intelligence

Advances in JD E‑commerce Advertising CTR Prediction: Variational Feature Learning, User Interest Network Optimization, and Global User Collaborative Modeling

This article presents JD's end‑to‑end improvements for advertising click‑through‑rate prediction, addressing cold‑start, deep user‑interest mining, and full‑domain collaborative information through a variational feature learning framework, enhanced interest networks (PPNet+, NeNet, Weighted‑MMoE) and exposure‑sequence modeling, achieving over 1% cumulative AUC gain and publication in top conferences.

CTR predictione-commerce recommendationmachine learning
0 likes · 21 min read
Advances in JD E‑commerce Advertising CTR Prediction: Variational Feature Learning, User Interest Network Optimization, and Global User Collaborative Modeling