coarse ranking | BestHub

DaTaobao Tech

Jan 6, 2023 · Artificial Intelligence

Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking

The paper presents a two‑stage e‑commerce search framework where the coarse‑ranking stage is redesigned with multi‑objective optimization, expanded negative sampling, and listwise distillation—guided by a new global transaction hitrate metric—enabling it to surpass fine‑ranking on large candidate sets and boost overall GMV by about one percent.

coarse rankinge-commercefine ranking

0 likes · 25 min read

Two‑Stage Ranking Optimization in E‑commerce Search: From Coarse to Fine Ranking

Tencent Cloud Developer

Apr 20, 2022 · Artificial Intelligence

Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization

Coarse ranking bridges recall and fine ranking by trimming tens of thousands of candidates to a few hundred or thousand using a three‑part framework—sample construction, ordinary and cross‑feature engineering, and evolving deep models—from rule‑based to lightweight MLPs, while employing distillation, feature crossing, pruning, quantization, and bias mitigation to balance accuracy with strict latency constraints.

Artificial IntelligenceFeature Engineeringcoarse ranking

0 likes · 9 min read

Coarse Ranking in Recommendation Systems: Architecture, Models, and Optimization

DataFunTalk

Mar 8, 2021 · Artificial Intelligence

Recommendation System Architecture and Coarse Ranking Design for Tencent Music's Quanmin K‑Song

This article details the business background, system architecture, coarse‑ranking algorithms, dual‑tower model, model distillation, diversity‑control techniques such as DPP, and the online performance gains of the recommendation pipeline used in Tencent Music's Quanmin K‑Song platform.

AIDPPModel Distillation

0 likes · 24 min read

Recommendation System Architecture and Coarse Ranking Design for Tencent Music's Quanmin K‑Song

DataFunTalk

Feb 27, 2021 · Artificial Intelligence

Optimizing Coarse Ranking Models for Short Video Recommendation: From GBDT to Dual‑Tower DNN and Cascading

This article details the practical upgrades of iQIYI's short‑video recommendation coarse‑ranking pipeline, moving from a GBDT model to a dual‑tower DNN, applying knowledge distillation, embedding compression, inference optimizations, and finally a cascade architecture to align with the fine‑ranking model while reducing resource consumption.

cascading modelcoarse rankingdual-tower DNN

0 likes · 12 min read

Optimizing Coarse Ranking Models for Short Video Recommendation: From GBDT to Dual‑Tower DNN and Cascading

iQIYI Technical Product Team

Feb 26, 2021 · Artificial Intelligence

Optimization of Coarse Ranking Models for Short‑Video Recommendation at iQIYI

iQIYI’s short‑video recommendation team replaced a GBDT coarse‑ranking model with a lightweight dual‑tower DNN, applied knowledge distillation, sparse‑aware embedding optimization, and inference merging, then introduced a cascade MMOE architecture, achieving comparable accuracy with half the memory, ~19 ms latency reduction, and measurable gains in watch time, CTR and engagement.

Online Inferencecascade modelcoarse ranking

0 likes · 15 min read

Optimization of Coarse Ranking Models for Short‑Video Recommendation at iQIYI