multi-objective optimization

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

JD Tech Talk

Mar 18, 2025 · Artificial Intelligence

Generative Recommendation for CPS Advertising: Intent Sensing, Multi‑Objective Optimization, and the One4All Framework

This article surveys recent advances in generative recommendation for CPS advertising, detailing explicit intent‑aware controllable product recommendation, multi‑objective optimization techniques based on reward‑in‑context and DPO, and the scalable One4All framework that unifies behavior and language modeling across diverse ad scenarios.

CPS advertisingLLMgenerative recommendation

0 likes · 14 min read

Generative Recommendation for CPS Advertising: Intent Sensing, Multi‑Objective Optimization, and the One4All Framework

JD Cloud Developers

Mar 18, 2025 · Artificial Intelligence

How Generative LLMs Are Transforming CPS Advertising Recommendations

Since large language models have excelled in NLP, researchers are now enhancing CPS advertising recommendation systems by integrating generative LLMs for explicit intent perception, multi‑objective optimization, and a unified One4All framework, achieving significant offline and online performance gains across click‑through, conversion, and revenue metrics.

CPS advertisingLLMgenerative recommendation

0 likes · 19 min read

How Generative LLMs Are Transforming CPS Advertising Recommendations

Xiaohongshu Tech REDtech

Mar 13, 2025 · Artificial Intelligence

UniCBE: A Unified Multi‑Objective Optimization Framework for Contrastive Based Evaluation

UniCBE introduces a unified multi‑objective optimization framework for contrastive‑based evaluation that mitigates sampling bias, unbalanced uncertainty reduction, and inefficient resource allocation by combining three decoupled probability matrices through a greedy and Hadamard‑product strategy, achieving Pearson correlations above 0.995 with only 83 % of the annotation budget and cutting evaluation costs by more than 50 % across diverse LLM evaluators.

Contrastive EvaluationLarge Language ModelsSampling Bias

0 likes · 10 min read

UniCBE: A Unified Multi‑Objective Optimization Framework for Contrastive Based Evaluation

JD Retail Technology

Feb 28, 2025 · Artificial Intelligence

Generative Recommendation with DPO Alignment for JD Alliance Advertising: Multi‑Objective Optimization and Online Results

The paper presents a generative recommendation framework for JD Alliance advertising that combines semantic‑ID modeling, large‑model pre‑training and fine‑tuning, and Direct Preference Optimization (including Softmax‑DPO and β‑DPO) to jointly boost click‑through and conversion rates, achieving +0.6% UCTR and +8% UCVR in online tests while outlining future multi‑objective extensions.

DPOLarge Language Modelsadvertising

0 likes · 12 min read

Generative Recommendation with DPO Alignment for JD Alliance Advertising: Multi‑Objective Optimization and Online Results

DataFunSummit

Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

E-commercecross-entropy methoddeep learning

0 likes · 14 min read

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

DataFunSummit

Jun 26, 2024 · Artificial Intelligence

2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration

This article outlines the current bottlenecks of conventional recommendation pipelines and proposes a comprehensive 2026 research agenda covering retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value modeling, whole‑site optimization, interactive recommendation, personalized modeling, decision‑theoretic formulation, and the OneRec multi‑source fusion framework.

Artificial IntelligenceLarge Language Modelsmulti-objective optimization

0 likes · 18 min read

2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration

Model Perspective

Jun 23, 2024 · Artificial Intelligence

Mastering Multi-Objective Optimization with NSGA-II: Theory and Python Example

This article introduces the fundamentals of multi‑objective optimization, explains the NSGA‑II algorithm’s non‑dominated sorting, crowding distance, and selection mechanisms, and demonstrates its application to a production‑line case study with a complete Python implementation and visualized Pareto front.

NSGA-IIPareto frontPython

0 likes · 10 min read

Mastering Multi-Objective Optimization with NSGA-II: Theory and Python Example

Model Perspective

May 17, 2024 · Operations

Designing Compromise Solutions with Multi‑Objective Optimization

This article introduces a mathematical model for designing compromise solutions in multi‑party decision making, explains the underlying multi‑objective optimization framework, presents a quadratic programming example, and discusses how adjusting indicator ranges can balance differing preferences to achieve mutually acceptable outcomes.

compromise modelingdecision makingmathematical modeling

0 likes · 6 min read

Designing Compromise Solutions with Multi‑Objective Optimization

DataFunTalk

Apr 3, 2024 · Artificial Intelligence

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

This presentation outlines the current bottlenecks of conventional recommendation pipelines and proposes a 2026 roadmap that includes retention improvement, user‑growth strategies, content‑ecosystem metrics, Pareto‑optimal multi‑objective optimization, long‑term value modeling, site‑wide spatial optimization, interactive recommendation, personalized modeling, and the integration of large‑model fusion through the OneRec framework.

Large Language Modelsinteractive recommendationmulti-objective optimization

0 likes · 18 min read

Future Directions of Recommendation Systems: Retention, User Growth, Content Ecosystem, Multi‑Objective Optimization, and Large‑Model Fusion

Sohu Tech Products

Dec 6, 2023 · Artificial Intelligence

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.

AlibabaReal-time Controlhypernetworks

0 likes · 34 min read

Real-time Controllable Multi-Objective Re-ranking Models for Taobao Feed Recommendation

DataFunTalk

Nov 14, 2023 · Artificial Intelligence

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

This article presents a comprehensive study of a controllable multi‑objective re‑ranking model for Taobao's information‑flow recommendation, detailing the challenges of complex feed scenarios, three modeling paradigms (V1‑V3), an actor‑critic reinforcement learning framework with hypernet‑generated weights, and extensive online evaluation results.

Real-time Controlhypernetworksmulti-objective optimization

0 likes · 31 min read

Real-Time Controllable Multi-Objective Re‑ranking for Taobao Feed

Model Perspective

Nov 4, 2023 · Operations

Pareto Optimality Explained: How to Balance Conflicting Goals

Pareto optimality, also known as Pareto efficiency, describes a state where improving any individual's outcome inevitably worsens another's, serving as a key criterion in multi‑objective optimization and decision science for evaluating trade‑offs such as maximizing profit while minimizing environmental impact.

Pareto optimalitydecision sciencemulti-objective optimization

0 likes · 5 min read

Pareto Optimality Explained: How to Balance Conflicting Goals

AntTech

Mar 13, 2023 · Cloud Computing

Cougar: A General Framework for Jobs Optimization in Cloud

Cougar is a cloud‑native, multi‑objective optimization framework that unifies metadata and monitoring ingestion to improve resource efficiency and performance for large‑scale AI and big‑data jobs, demonstrating over 50% CPU‑memory savings and stable latency in production experiments.

Artificial Intelligencebig datacloud-computing

0 likes · 10 min read

Cougar: A General Framework for Jobs Optimization in Cloud

Alimama Tech

Apr 6, 2022 · Artificial Intelligence

Intelligent Auction Mechanisms for Alibaba Display Advertising: AIDA Framework, Deep GSP, and Neural Auction

Alibaba’s AIDA framework combines a bidding‑agent layer and a novel auction layer—Deep GSP and Neural Auction—to allocate display ads across its ecosystem, achieving incentive‑compatible, multi‑objective optimization, higher ROI, and scalable deployment via TensorFlow‑based platform services.

advertisingauctiondeep learning

0 likes · 16 min read

Intelligent Auction Mechanisms for Alibaba Display Advertising: AIDA Framework, Deep GSP, and Neural Auction

DataFunSummit

Mar 25, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's e‑commerce recommendation system, detailing its four‑stage ranking pipeline, multi‑objective optimization with personalized fusion, transformer‑based user behavior sequence modeling, fine‑grained behavior modeling, and multimodal feature integration, and shares experimental results and engineering optimizations.

E-commercemulti-objective optimizationmultimodal features

0 likes · 17 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Ranking, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

Alimama Tech

Mar 16, 2022 · Artificial Intelligence

Deep GSP: Multi‑Objective Deep Learning Based Advertising Auction Mechanism

Deep GSP is a multi‑objective, deep‑learning ad auction that jointly learns rank scores while enforcing game‑theoretic constraints—monotonicity, incentive compatibility, and Nash equilibrium—and a smooth‑transition penalty, using DDPG reinforcement learning to outperform traditional GSP across revenue, clicks, conversions, and add‑to‑cart metrics.

advertising auctiondeep learningmechanism design

0 likes · 18 min read

Deep GSP: Multi‑Objective Deep Learning Based Advertising Auction Mechanism

DataFunTalk

Mar 14, 2022 · Artificial Intelligence

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

The article presents JD's end‑to‑end recommendation pipeline, detailing the four‑stage ranking chain, challenges of fine‑ranking, and practical solutions including multi‑objective learning, transformer‑based user behavior sequence modeling, fine‑grained click behavior integration, and multimodal image features, with offline and online performance gains.

E-commercefine-grained behaviormulti-objective optimization

0 likes · 18 min read

Advanced Practices in E‑commerce Recommendation: Multi‑Objective Optimization, User Behavior Sequence Modeling, Fine‑Grained Behavior Modeling, and Multimodal Features

Alimama Tech

Oct 13, 2021 · Artificial Intelligence

Multi-Agent Cooperative Bidding Game Framework for Multi-Objective Optimization in Online Advertising

The paper presents MACG, a multi‑agent cooperative bidding game that integrates a global objective with individual advertiser goals, derives optimal bidding formulas, employs a strategy network and evolutionary search to tune parameters, and demonstrates over‑5% metric gains and stable 15‑day performance in Taobao’s online advertising platform.

Multi-Agent Reinforcement LearningReal-Time BiddingTaobao advertising platform

0 likes · 18 min read

Multi-Agent Cooperative Bidding Game Framework for Multi-Objective Optimization in Online Advertising

DataFunTalk

Oct 4, 2021 · Artificial Intelligence

Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization

This article details how 58 Community improved content value share, click‑through, and user retention by designing a generalized multi‑objective recommendation algorithm that leverages cross‑domain embeddings, DeepFM‑DIN models, EGES‑inspired pre‑training, and online CEM‑based parameter optimization.

CEMcross-domain embeddingdeep learning

0 likes · 16 min read

Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization