How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning

This article details the EMER framework—a Transformer‑based, end‑to‑end multi‑objective ranking system that replaces handcrafted formulas with a learnable AI model, introduces relative‑satisfaction signals and dynamic loss weighting, and demonstrates significant offline and online performance gains in Kuaishou's short‑video recommendation pipeline.

AIRecommendation Systemsmulti-objective learning

0 likes · 16 min read

How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning

Dada Group Technology

Nov 18, 2022 · Artificial Intelligence

JD Daojia Machine Learning Platform: Architecture and Implementation

This article introduces JD Daojia's machine learning platform, detailing its architecture, implementation principles, and practical applications in various business scenarios, achieving significant improvements in recommendation and search systems.

Deep LearningDistributed TrainingKubernetes

0 likes · 28 min read

JD Daojia Machine Learning Platform: Architecture and Implementation

DeWu Technology

Jul 1, 2022 · Artificial Intelligence

Multi-Objective Ranking with Deep Interest Transformer for Tabular Product Recommendation

The Dewu app’s new multi‑objective ranking model replaces the shallow ESMM baseline with a DeepFM‑based MLP and a Deep Interest Transformer that encodes up to 120 recent user actions, adds a dedicated bias network, and fuses short‑ and long‑term interests, achieving modest CTR and CVR AUC improvements while planning future tab‑specific extensions.

CTRCVRbias net

0 likes · 13 min read

Multi-Objective Ranking with Deep Interest Transformer for Tabular Product Recommendation

DataFunSummit

Mar 17, 2022 · Artificial Intelligence

Optimizing QQ Music Ranking Model: From User Perception to Multi‑Category Traffic Exploration

This talk presents the evolution of QQ Music's ranking system, detailing background challenges, user‑perception modeling, multi‑objective and causal learning to mitigate the Matthew effect, long‑tail content support, cross‑domain recommendation, and module personalization for diversified traffic, concluding with future research directions.

causal inferencecross-domain recommendationmulti-objective learning

0 likes · 16 min read

Optimizing QQ Music Ranking Model: From User Perception to Multi‑Category Traffic Exploration

DataFunTalk

Feb 14, 2022 · Artificial Intelligence

Optimizing QQ Music Ranking Models: From Pairwise Methods to Multi‑Objective Learning and Causal Inference

This talk details the evolution of QQ Music's ranking system, covering background, user‑perception modeling, pairwise optimization, advanced model architectures, multi‑objective learning with causal inference to mitigate the Matthew effect, cross‑domain recommendation, and module personalization that together boost user engagement and platform traffic.

cross-domain recommendationmulti-objective learningpairwise learning

0 likes · 16 min read

Optimizing QQ Music Ranking Models: From Pairwise Methods to Multi‑Objective Learning and Causal Inference

DataFunTalk

Sep 4, 2021 · Artificial Intelligence

Real‑time Positive/Negative Feedback Sequence Modeling and Multi‑objective Optimization for Taobao Live Ranking

This article presents a practical study on modeling real‑time positive and negative feedback sequences and applying multi‑objective optimization in the re‑ranking stage of Taobao Live, detailing system architecture, feature engineering, loss design, experimental results, and future research directions.

Taobao Livee‑commercefeedback modeling

0 likes · 12 min read

Real‑time Positive/Negative Feedback Sequence Modeling and Multi‑objective Optimization for Taobao Live Ranking

DataFunTalk

Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Recommendation SystemsTransformerbias mitigation

0 likes · 22 min read

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688