multi-task learning

JD Tech

May 6, 2025 · Artificial Intelligence

One4All Generative Recommendation Framework for CPS Advertising

This article reviews recent advances in applying large language models to CPS advertising recommendation, outlines business requirements and core technical challenges, proposes an extensible multi‑task generative framework with explicit intent perception and multi‑objective optimization, and presents offline and online performance gains along with future research directions.

AI optimizationCPS AdvertisingGenerative Models

0 likes · 13 min read

One4All Generative Recommendation Framework for CPS Advertising

JD Retail Technology

Apr 2, 2025 · Artificial Intelligence

One4All: A Scalable Multi‑Task Generative Recommendation Framework for CPS Advertising

The paper introduces One4All, a scalable multi‑task generative recommendation framework for CPS advertising that combines few‑shot intent prompting, a Rewards‑in‑Context multi‑objective optimization, and an online model‑selection strategy, delivering 2‑3× offline HitRate/NDCG gains and notable online CTR, CVR, and commission improvements.

LLMLarge Language Modelsadvertising

0 likes · 14 min read

One4All: A Scalable Multi‑Task Generative Recommendation Framework for CPS Advertising

AntTech

Jan 13, 2025 · Artificial Intelligence

Two Ant Group Papers Selected for AAAI 2025: Human‑Feedback Evaluation Framework for Product Image Background Inpainting and Bagging‑Expert Network for Multi‑Task Learning

Two Ant Group papers accepted at AAAI 2025—one presenting a human‑feedback‑driven evaluation framework for product image background inpainting using EfficientSAM and a new HFPC‑44k dataset, and the other proposing a Bagging‑Expert Network to mitigate expert polarization in multi‑gate mixture‑of‑experts for multi‑task learning.

AAAI 2025Ant GroupBagging-Expert Network

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Two Ant Group Papers Selected for AAAI 2025: Human‑Feedback Evaluation Framework for Product Image Background Inpainting and Bagging‑Expert Network for Multi‑Task Learning

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

Zhuanzhuan Tech

Sep 19, 2024 · Artificial Intelligence

Multi-Task Learning for Category Prediction in Zhaozhuan Search Intent Understanding

This article introduces multi‑task learning, reviews industry category‑prediction methods, and details Zhaozhuan's practical application of MTL to improve e‑commerce search intent understanding through hierarchical category, brand, and model prediction using RoBERTa and contrastive learning.

Artificial IntelligenceCategory PredictionNLP

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Multi-Task Learning for Category Prediction in Zhaozhuan Search Intent Understanding

Tencent Advertising Technology

Jul 17, 2024 · Artificial Intelligence

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

This article summarizes Tencent Advertising's recent research on recommendation models, covering comprehensive feature encoding techniques, solutions to embedding dimensional collapse through multi‑embedding paradigms, and novel methods such as STEM and AME to disentangle conflicting user interests across multiple tasks.

dimensional collapseembeddingfeature encoding

0 likes · 20 min read

Ads Recommendation in a Collapsed and Entangled World: Tencent's Innovations in Feature Encoding, Dimensional Collapse Mitigation, and Interest Disentanglement

AntTech

Jun 12, 2024 · Artificial Intelligence

Introducing C-Poly: A Multi‑Task Learning Paradigm for More Efficient Large‑Model Training

The article introduces the ICLR‑2024 paper C‑Poly, a multi‑task learning framework that boosts large‑model efficiency and resource utilization, aiming to make powerful AI models as accessible and convenient as everyday services like QR‑code payments.

AI EfficiencyC-PolyICLR2024

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Introducing C-Poly: A Multi‑Task Learning Paradigm for More Efficient Large‑Model Training

DataFunSummit

Apr 15, 2024 · Artificial Intelligence

Deep Learning Practices for Internet Real‑Estate Recommendation at 58.com

This article details the end‑to‑end deep‑learning pipeline used by 58.com for real‑estate recommendation, covering business background, a six‑layer architecture, vector‑based recall, various embedding and ranking models, multi‑task and multi‑scenario optimization techniques, and future directions for large‑model integration.

Vector Searchdeep learningfaiss

0 likes · 19 min read

Deep Learning Practices for Internet Real‑Estate Recommendation at 58.com

DataFunTalk

Mar 28, 2024 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario recommendation algorithms, detailing background challenges, algorithm classifications such as TAML, CausalInt, and DFFM, their modular designs, experimental validations, and practical Q&A insights for large‑scale advertising systems.

Recommendation systemsadvertising algorithmsmachine learning

0 likes · 19 min read

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

DataFunSummit

Feb 9, 2024 · Artificial Intelligence

STAN: A User‑Lifecycle‑Based Multi‑Task Recommendation Model for Shopee

The article introduces STAN, a multi‑task recommendation framework that leverages user lifecycle segmentation to jointly optimize CTR, stay‑time, and CVR, detailing the business context, key challenges, solution architecture, offline and online evaluations, and future research directions.

CVRRecommendation systemsctr

0 likes · 8 min read

STAN: A User‑Lifecycle‑Based Multi‑Task Recommendation Model for Shopee

DataFunSummit

Dec 27, 2023 · Artificial Intelligence

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

This article presents a two‑stage constrained actor‑critic (TSCAC) algorithm that models short‑video recommendation as a constrained reinforcement‑learning problem, details its theoretical formulation and optimization loss, and validates its superiority through extensive offline and online experiments, followed by a multi‑task reinforcement‑learning framework (RMTL) that further improves multi‑objective recommendation performance.

Recommendation systemsconstrained optimizationmulti-task learning

0 likes · 16 min read

Two-Stage Constrained Actor-Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Framework

DataFunSummit

Dec 18, 2023 · Artificial Intelligence

Click-aware Structure Transfer with Sample Weight Assignment (CSTWA) for Multi‑task CVR Optimization

This article reviews Shopee and Tsinghua University's latest work on multi‑task CVR optimization, introducing the Click‑aware Structure Transfer with Sample Weight Assignment (CSTWA) method, which tackles knowledge sharing and conflict between CTR and CVR through a three‑part architecture, and demonstrates its superior performance on industrial and public datasets.

CVRRecommendation systemsStructure Transfer

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Click-aware Structure Transfer with Sample Weight Assignment (CSTWA) for Multi‑task CVR Optimization

DataFunSummit

Dec 3, 2023 · Artificial Intelligence

Shopee Live Personalized CTR Optimization via Calibration‑Based Meta‑Learning

This article presents Shopee's calibration‑based meta‑learning approach for personalized click‑through‑rate prediction in live streaming, detailing business context, modeling goals, model evolution from Calibration4CVR to CBMR, EmbCB and MlpCB optimizations, and multi‑task and multi‑scene extensions that achieve significant AUC and business metric improvements.

Shopeectrmeta-learning

0 likes · 11 min read

Shopee Live Personalized CTR Optimization via Calibration‑Based Meta‑Learning

DataFunTalk

Nov 27, 2023 · Artificial Intelligence

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

This article introduces STAN, a user‑lifecycle‑aware multi‑task recommendation model proposed by Shopee that refines CTR, CVR, and stay‑time predictions by identifying and tracking user states, demonstrates offline gains on Shopee and public datasets, and reports online improvements in click‑through, dwell‑time, and order metrics.

CVRRecommendation systemsctr

0 likes · 8 min read

STAN: A User‑Lifecycle‑Aware Multi‑Task Recommendation Model for Shopee

DataFunTalk

Nov 9, 2023 · Artificial Intelligence

Multi‑Task and Multi‑Scenario Algorithms for Recommendation Systems – A Huawei Case Study

This article presents Huawei's research on applying multi‑task and multi‑scenario learning to advertising recommendation, covering background challenges, algorithm taxonomy, detailed designs of TAML, CausalInt and DFFM models, experimental results, and a Q&A session.

AIHuaweiadvertising

0 likes · 20 min read

Multi‑Task and Multi‑Scenario Algorithms for Recommendation Systems – A Huawei Case Study

Sohu Tech Products

Nov 8, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

The presentation introduces a two‑stage constrained actor‑critic algorithm that learns auxiliary policies for interaction signals before optimizing watch‑time under KL constraints, and a reinforcement‑learning multi‑task learning framework that models session‑level dynamics with adaptive multi‑critic weighting, both achieving significant offline and online gains in short‑video recommendation.

Recommendation systemsactor-criticconstrained optimization

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic for Short‑Video Recommendation and a Reinforcement‑Learning Multi‑Task Recommendation Framework

Zhuanzhuan Tech

Nov 7, 2023 · Artificial Intelligence

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

This article outlines the challenges of multi-task and multi-scenario modeling for large-scale C-end services, reviews key industry approaches such as Shared-Bottom, MMoE, PLE, ESMM, and LHUC, and details Zhaozhuan’s own EPNET-based solution that improved click-through and conversion rates.

CVRRecommendation systemsctr

0 likes · 13 min read

Multi-Task Multi-Scenario Modeling: Challenges, Industry Solutions, and Zhaozhuan's Practice

DataFunTalk

Nov 6, 2023 · Artificial Intelligence

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

This article presents a two‑stage constrained actor‑critic reinforcement learning algorithm for short‑video recommendation, models the problem as a constrained MDP, details the algorithm’s stages, and reports extensive offline and online experiments showing superior watch‑time and interaction metrics, followed by a multi‑task RL framework and its evaluations.

Recommendation systemsconstrained optimizationmulti-task learning

0 likes · 16 min read

Two‑Stage Constrained Actor‑Critic Reinforcement Learning for Short‑Video Recommendation and a Multi‑Task RL Framework

DataFunSummit

Oct 9, 2023 · Artificial Intelligence

Multi-Task and Multi-Scenario Algorithms for Recommendation Systems: Methods, Challenges, and Applications

This article presents a comprehensive overview of multi‑task and multi‑scenario algorithms applied to recommendation systems, covering background challenges, algorithm taxonomy, recent research, detailed model architectures such as TAML, CausalInt and DFFM, experimental results on public and private datasets, and a Q&A discussion.

Recommendation systemsadvertisingmachine learning

0 likes · 20 min read

Kuaishou Tech

Aug 11, 2023 · Artificial Intelligence

PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation

The paper introduces PEPNet, a plug‑and‑play network that tackles the domain‑seesaw and task‑seesaw problems in multi‑scenario recommendation by using a gated personalization module (GateNU) together with embedding‑level (EPNet) and parameter‑level (PPNet) personalization, and demonstrates its superiority through extensive offline and online experiments on Kuaishou data.

deep learningembeddinggate network

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PEPNet: Parameter and Embedding Personalized Network for Multi‑Task Multi‑Domain Recommendation