ESMM | BestHub

Zhuanzhuan Tech

Nov 6, 2024 · Artificial Intelligence

Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario

This article reviews the necessity, benefits, and practical implementations of multi-task learning in e‑commerce search, detailing model selection, architecture extensions such as ESMM and ESM², and future directions for handling user behavior sequences and multi‑objective optimization.

ESMMModel ArchitectureRecommendation systems

0 likes · 13 min read

Multi-Task Learning for E-commerce Search: Overview, Practices, and Model Design in the Zhuanzhuan Scenario

Tongcheng Travel Technology Center

Apr 14, 2023 · Artificial Intelligence

Multi‑Business Recommendation System for the Tongcheng App Home Page Waterfall Flow

This article describes the architecture, data processing, city‑intent modeling, resource recall strategies, and multi‑task ranking models—including PLE‑CGC and ESMM—used to improve click‑through and conversion rates of the Tongcheng travel app's homepage waterfall‑flow recommendation, and outlines experimental results and future optimization directions.

CVRESMMPLE

0 likes · 10 min read

Multi‑Business Recommendation System for the Tongcheng App Home Page Waterfall Flow

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

DataFunTalk

May 6, 2022 · Artificial Intelligence

Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation

This article introduces the ESMM (Entire Space Multi‑Task Model) proposed by Alibaba, explaining how it tackles sample selection bias and data sparsity in post‑click conversion rate (CVR) prediction through shared embeddings and implicit pCVR learning, and provides a detailed implementation using the EasyRec framework with code examples.

CVR PredictionESMMRecommendation systems

0 likes · 11 min read

Entire Space Multi‑Task Model (ESMM) for Post‑Click Conversion Rate Estimation

DataFunTalk

Sep 19, 2021 · Artificial Intelligence

Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans

This article presents the end‑to‑end practice of a second‑hand housing recommendation system at 58.com and Anjuke, covering business background, embedding‑based vector recall, multi‑objective ranking methods such as ESMM and MMOE, experimental results, and future development directions.

ESMMReal Estateembedding

0 likes · 14 min read

Second‑hand Housing Recommendation System: Business Background, Vector Recall, Multi‑objective Optimization and Future Plans

DataFunTalk

Sep 20, 2019 · Artificial Intelligence

Multi‑Task Learning for Joint CTR, CVR, and GMV Prediction in E‑commerce

This article describes how a multi‑task learning framework based on ESMM and attention‑shared embeddings was built to jointly predict click‑through rate, conversion rate, and gross merchandise value in a large e‑commerce platform, addressing data sparsity, bias, and training challenges.

AttentionCVRESMM

0 likes · 8 min read

Multi‑Task Learning for Joint CTR, CVR, and GMV Prediction in E‑commerce