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.

Generative Searche-commerce recommendationlong-tail distribution

0 likes · 13 min read

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

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

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 recommendationmulti-task 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

Yanxuan Tech Team

Sep 25, 2020 · Artificial Intelligence

How Vector Embeddings Power E‑Commerce Search and Recommendation at NetEase Yanxuan

This article explains how Yanxuan built a comprehensive vector system—from product embeddings and graph models to large‑scale similarity computation—and applied it across search, recommendation, and purchase prediction tasks, highlighting practical algorithms, infrastructure, and future directions.

e-commerce recommendationmachine learningsearch ranking

0 likes · 18 min read

How Vector Embeddings Power E‑Commerce Search and Recommendation at NetEase Yanxuan

Alibaba Cloud Developer

Jul 20, 2018 · Artificial Intelligence

How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU

This article presents Alibaba’s first brand‑level ranking system that personalizes product ordering by modeling user brand preferences with an enhanced Attention‑GRU, detailing feature engineering, model improvements, extensive offline experiments on a massive Tmall dataset, and a successful online A/B test that increased CTR, ATIP, and GMV.

Deep Learningattention GRUbrand ranking

0 likes · 27 min read

Alibaba Cloud Developer

Jul 18, 2018 · Artificial Intelligence

How Alibaba’s Brand‑Level Ranking Boosts E‑Commerce Clicks with Attention‑GRU

This paper presents Alibaba's first brand‑level ranking system that personalizes brand ordering on e‑commerce platforms by designing brand features and extending an Attention‑GRU model with three key improvements, demonstrating significant offline and online performance gains on the Tmall marketplace.

attention GRUbrand rankinge-commerce recommendation

0 likes · 27 min read