MMoE | BestHub

DataFunTalk

Nov 1, 2022 · Artificial Intelligence

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

This article presents the practical evolution of NetEase Yanxuan's recommendation pipeline, covering background, multi‑objective and cross‑domain modeling, bias correction, loss function enhancements, long‑term value strategies, and multi‑scene modeling, with experimental results and a Q&A session.

AICross-DomainMMoE

0 likes · 20 min read

Cross‑Domain Multi‑Objective Modeling and Long‑Term Value Exploration in NetEase Yanxuan Recommendation System

NetEase Cloud Music Tech Team

Aug 17, 2022 · Artificial Intelligence

Live Streaming Recommendation Practices in NetEase Cloud Music: Real-time, Multi-target, and Multimodal Approaches

The paper describes NetEase Cloud Music’s LOOK live‑streaming recommendation system for the song‑playback page, which combines millisecond‑level real‑time feature pipelines, multi‑target optimization (click, watch, gift, comment) via ESMM+FM and MMoE models, GradNorm‑based loss fusion, and a multimodal avatar‑text‑host ranking model, achieving double‑digit CTR and CTCVR gains while balancing producer and consumer retention.

ESMMGradNormLive Streaming

0 likes · 26 min read

Live Streaming Recommendation Practices in NetEase Cloud Music: Real-time, Multi-target, and Multimodal Approaches

DaTaobao Tech

Mar 29, 2022 · Artificial Intelligence

Dynamic Weight Averaging and Gradient Normalization for Multi‑Task Recommendation Models

To improve multi‑task recommendation in the “每平每屋” system, the team augments an MMoE ranking model with dynamic weight averaging, dynamic task prioritization, and GradNorm gradient normalization, stabilizing loss convergence across CTR, CVR, and fav tasks and delivering 3–4% online metric gains.

A/B testingDynamic Weight AveragingGradient Normalization

0 likes · 10 min read

Dynamic Weight Averaging and Gradient Normalization for Multi‑Task Recommendation Models

Baidu Geek Talk

Sep 6, 2021 · Artificial Intelligence

Short Video Recommendation System Design: Baidu Haokan Video Practice

The article outlines Baidu Haokan’s short‑video recommendation architecture, describing how a unified ranking pipeline uses user‑interest signals, multi‑objective MMOE and deep‑fusion models, and long‑term value estimation to balance personalized user experience, creator exposure, and advertiser goals across billions of daily video plays.

BaiduMMoElong-term value

0 likes · 8 min read

Short Video Recommendation System Design: Baidu Haokan Video Practice

DataFunSummit

Sep 2, 2021 · Artificial Intelligence

Multi‑Task Learning Models for Recommendation Systems: An Industrial Survey

This article surveys recent industrial multi‑task learning approaches for recommendation, covering models such as Alibaba's ESMM and ESM2, DUPN, Meituan's deep ranking, Google’s MMoE, YouTube’s multi‑objective system, Zhihu’s ranking, and summarizing their architectures, loss functions, and practical gains.

CVRMMoERecommendation systems

0 likes · 15 min read

Multi‑Task Learning Models for Recommendation Systems: An Industrial Survey

58 Tech

Aug 5, 2021 · Artificial Intelligence

58 City AI Algorithm Competition – Updates, Leaderboard, and Baseline Model

The 58 City AI Algorithm Competition, now in its second edition, has attracted over 200 teams from more than 50 universities and 30 internal groups, with daily leaderboard updates, a baseline MMoE model scoring 0.7294, and a GitHub repository for participants.

58 CityAI competitionMMoE

0 likes · 5 min read

58 City AI Algorithm Competition – Updates, Leaderboard, and Baseline Model

58 Tech

Jul 23, 2021 · Artificial Intelligence

MMoE Model Training and Evaluation for 58.com Recruitment Recommendation Competition

This article details the background, MMoE model architecture, baseline setup, environment configuration, data preprocessing, training process, evaluation results, and department information for the 58.com recruitment recommendation AI competition using the WPAI platform.

AI competitionMMoEPyTorch

0 likes · 11 min read

MMoE Model Training and Evaluation for 58.com Recruitment Recommendation Competition

DataFunTalk

Mar 2, 2021 · Artificial Intelligence

Multi-Objective Optimization with MMoE for Taobao "Lying Flat" Channel

This article presents the design and implementation of a multi‑objective optimization framework using Multi‑gate Mixture‑of‑Experts (MMoE) to improve click‑through, conversion, and purchase behaviors in Taobao's "Lying Flat" home‑goods recommendation channel, detailing model variants, feature engineering, loss weighting, and online A/B test results.

CVRMMoEctr

0 likes · 10 min read

Multi-Objective Optimization with MMoE for Taobao "Lying Flat" Channel