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Apr 6, 2026 · Artificial Intelligence

How TIR‑Agent Turns Image‑Restoration Tools into a Learnable Decision‑Making Agent

The paper introduces TIR‑Agent, an image‑restoration agent that learns a tool‑calling policy via supervised fine‑tuning and reinforcement learning, addressing exploration stagnation and multi‑objective reward imbalance, and demonstrates over 2.5× faster inference and superior multi‑metric performance on synthetic and real degradation datasets.

Computer VisionImage RestorationReinforcement Learning

0 likes · 18 min read

How TIR‑Agent Turns Image‑Restoration Tools into a Learnable Decision‑Making Agent

Tencent Advertising Technology

Jan 8, 2026 · Artificial Intelligence

How Tencent Boosted Ad Experience by Up to 20% Using Reinforcement‑Learning‑Based Ranking

Tencent's ad tech team redesigned its ad ranking system by adding a parallel user‑experience‑optimized pipeline and evolving from manual CEM tuning to DDPG‑based reinforcement learning, achieving 10‑20% improvements in CTR, repeat‑view rates, and other experience metrics while maintaining overall spend.

AdvertisingReinforcement LearningUser experience

0 likes · 17 min read

How Tencent Boosted Ad Experience by Up to 20% Using Reinforcement‑Learning‑Based Ranking

Model Perspective

Oct 7, 2025 · Fundamentals

Unlock Life’s Success: The Three Powers of Cognition, Choice & Growth

This article treats life as an optimization problem and breaks it into three core forces—cognition, choice, and growth—showing how Bayesian inference, multi‑objective optimization, and dynamic system theory can model their interactions, guide decision‑making, and illustrate the feedback loops that drive personal development.

Bayesian inferencePersonal Developmentdecision making

0 likes · 8 min read

Unlock Life’s Success: The Three Powers of Cognition, Choice & Growth

Model Perspective

Oct 4, 2025 · Operations

How to Optimize Your Life with Scheduling Theory and Mathematical Models

This article treats personal life decisions as scheduling problems, presenting time, resource, priority, multi‑objective, stochastic, and robust optimization models—complete with objective functions, constraints, and solution approaches—to demonstrate how mathematical modeling can provide quantitative guidance for balancing work, health, learning, and happiness.

Schedulinglife optimizationmathematical modeling

0 likes · 10 min read

How to Optimize Your Life with Scheduling Theory and Mathematical Models

Data Party THU

Sep 2, 2025 · Artificial Intelligence

Gradient-Based Multi-Objective Deep Learning: Theory, Algorithms, and LLM Applications

This tutorial provides a systematic overview of gradient‑based multi‑objective optimization for deep learning, covering core solution strategies, algorithmic details, convergence and generalization analyses, and demonstrates how these methods can be applied to fine‑tune and align large language models.

Deep LearningGradient MethodsLLM fine-tuning

0 likes · 3 min read

Gradient-Based Multi-Objective Deep Learning: Theory, Algorithms, and LLM Applications

Model Perspective

Sep 1, 2025 · Operations

Mathematical Secrets Behind a Perfect Military Parade

This article explores how mathematical models—ranging from matrix representations of formations to error analysis, phase synchronization, timing control, perspective geometry, and multi‑objective optimization—can be applied to design, evaluate, and perfect military parades.

Visual Perceptionformation optimizationmathematical modeling

0 likes · 6 min read

Mathematical Secrets Behind a Perfect Military Parade

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.

AdvertisingLLMLarge Language Models

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 advertisingGenerative RecommendationLLM

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 advertisingGenerative RecommendationLLM

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 EvaluationSampling Biasefficiency

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.

AdvertisingDPOGenerative Recommendation

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.

Deep LearningReinforcement Learningcross-entropy method

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.

Large Language ModelsUser Retentionmulti-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.

Evolutionary AlgorithmsNSGA-IIPareto Front

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.

Operations Researchcompromise modelingdecision making

0 likes · 6 min read

Designing Compromise Solutions with Multi‑Objective Optimization

NewBeeNLP

Apr 8, 2024 · Artificial Intelligence

What Will Recommendation Systems Look Like in 2026? Emerging Trends and Challenges

This article analyzes the current bottlenecks of conventional recommendation systems and outlines ten forward‑looking research directions for 2026, including retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value estimation, site‑wide optimization, interactive recommendation, personalized modeling, decision‑theoretic framing, and the integration of large language models via the OneRec framework.

Large Language ModelsUser Retentioninteractive recommendation

0 likes · 18 min read

What Will Recommendation Systems Look Like in 2026? Emerging Trends and Challenges

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 ModelsRecommendation SystemsUser Retention

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 ControlRecommendation Systems

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 ControlRecommendation SystemsReinforcement Learning

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.

Operations ResearchPareto optimalitydecision science

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 IntelligenceResource Managementcloud computing

0 likes · 10 min read

Cougar: A General Framework for Jobs Optimization in Cloud

Alibaba Cloud Developer

Nov 25, 2022 · Artificial Intelligence

How Multi‑Objective Optimization Boosted Taobao Search’s Coarse Ranking

This report details the multi‑stage architecture of Taobao’s main search, introduces a new global‑transaction hitrate metric, analyzes offline and online evaluation gaps, and presents a series of model, loss‑function, and sampling improvements that together lifted overall conversion by about one percent.

coarse rankinge‑commercemachine learning

0 likes · 26 min read

How Multi‑Objective Optimization Boosted Taobao Search’s Coarse Ranking

Hulu Beijing

Nov 18, 2022 · Artificial Intelligence

How Video Search Engines Rank Results: From Click Models to Multi‑Goal Optimization

This article explains the architecture of video search engine ranking, covering optimization objectives such as relevance, click‑through rate and watch time, and detailing pointwise, pairwise and listwise learning approaches, model training pipelines, and online serving strategies.

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.

CTRCVRDeep Learning

0 likes · 10 min read

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

DataFunTalk

Jul 21, 2020 · Artificial Intelligence

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

This article details the end‑to‑end technical architecture of WeChat's "Look" personalized recommendation service, covering data collection, recall, multi‑stage ranking, various CTR and multi‑objective models, reinforcement‑learning based mixing, diversity optimization, and the engineering hurdles overcome to deploy these solutions at massive scale.

CTR predictionDeep LearningReinforcement Learning

0 likes · 17 min read

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

Youku Technology

Jun 15, 2020 · Artificial Intelligence

Multi-Objective Optimization for Guaranteed Delivery in Video Service Platforms

The paper proposes a two‑stage framework that first fits a differential‑equation‑based exposure‑click (P2C) model for each new video and then uses a genetic‑algorithm multi‑objective optimization to allocate scarce scene‑level exposure slots, simultaneously maximizing total views and halving CTR variance while outperforming manual baselines.

KDDODE modelingclick prediction

0 likes · 8 min read

Multi-Objective Optimization for Guaranteed Delivery in Video Service Platforms

Alibaba Cloud Developer

Jun 11, 2020 · Artificial Intelligence

How to Maximize Video Views with a Multi‑Objective Exposure Optimization Model

This article presents a data‑driven approach for allocating limited video exposure resources by building a PV‑click‑CTR (P2C) sensitivity model and a multi‑objective optimization framework that balances overall view volume and fairness across scenes, validated through offline metrics and online bucket tests.

Big Dataalgorithmexposure optimization

0 likes · 9 min read

How to Maximize Video Views with a Multi‑Objective Exposure Optimization Model

Youku Technology

May 21, 2020 · Artificial Intelligence

Multi‑objective Optimization for Guaranteed Delivery in a Video Service Platform

The KDD 2020 paper from Alibaba Entertainment presents a differential‑equation‑based hot‑content exposure sensitivity model and a multi‑objective optimization framework that, under exposure‑resource constraints, guarantees video delivery by accounting for nonlinear content exposure, timing, strategies, and user click habits, now deployed on Youku.

KDD 2020differential equationsexposure modeling

0 likes · 2 min read

Multi‑objective Optimization for Guaranteed Delivery in a Video Service Platform

Beike Product & Technology

Mar 22, 2020 · Artificial Intelligence

Adaptive Grid Multi‑Objective Particle Swarm Optimization for Exhibition Slot Allocation with Shell Score Integration

This document presents a multi‑objective optimization project that integrates the Shell Score credit system into exhibition slot allocation using adaptive‑grid based AG‑MOPSO, evaluates several swarm‑intelligence algorithms (ABC, ACO, PSO, MOPSO), and details the algorithm design, implementation steps, and experimental results across multiple cities.

AIParticle Swarm Optimizationadaptive grid

0 likes · 12 min read

Adaptive Grid Multi‑Objective Particle Swarm Optimization for Exhibition Slot Allocation with Shell Score Integration

DataFunTalk

Oct 14, 2019 · Artificial Intelligence

Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements

This article presents a comprehensive overview of short‑video recommendation at UC, covering business background, system architecture, the evolution from LR to Wide & Deep models, multi‑objective loss design with positive‑sample weighting, graph‑embedding fusion, time‑weighted loss, continuity modeling, a Boosting‑based WnD solution, and future research directions.

Deep Learningboostinggraph embedding

0 likes · 11 min read

Advances in Short Video Recommendation: Multi‑Objective Optimization and Model Enhancements

Alibaba Cloud Developer

Aug 23, 2018 · Artificial Intelligence

Boost Short-Video Recommendations: Multi-Goal Optimization with Weighted Logistic Regression

Alibaba's short‑video recommendation team details how they enhance both click‑through rate and viewing duration by applying sample reweighting, weighted logistic regression, and playback‑completion‑rate normalization, achieving over 6% offline AUC gains and more than 10% increase in average user watch time.

click-through ratemachine learningmulti-objective optimization

0 likes · 10 min read

Boost Short-Video Recommendations: Multi-Goal Optimization with Weighted Logistic Regression

Alibaba Cloud Developer

Aug 22, 2018 · Artificial Intelligence

Boosting Short Video Recommendations with Multi‑Objective Weighted Logistic Regression

This article explains how short‑video platforms enhance recommendation quality by combining click‑through‑rate models with multi‑objective optimization of watch time and completion rate, using sample reweighting and weighted logistic regression to balance perceived and real relevance while improving offline AUC and online user engagement.

CTRmulti-objective optimizationrecommendation

0 likes · 10 min read

Boosting Short Video Recommendations with Multi‑Objective Weighted Logistic Regression

AntTech

Jun 15, 2018 · Artificial Intelligence

AutoPilot: AI‑Driven Unmanned Risk Control in Alipay’s AlphaRisk System

The article explains how Alipay’s AutoPilot module leverages AI, semi‑supervised and evolutionary algorithms to achieve autonomous, personalized risk‑control decisions, optimizing multi‑objective trade‑offs and dramatically improving fraud loss rates during high‑traffic events like the 2017 Double‑11 sale.

AIAlipayAutoPilot

0 likes · 7 min read

AutoPilot: AI‑Driven Unmanned Risk Control in Alipay’s AlphaRisk System

Architects Research Society

Dec 17, 2015 · Artificial Intelligence

How Search Engine Experience Informs Personalized Recommendation at Toutiao

The article explains how search engine techniques such as large‑scale candidate recall, fine‑grained ranking, user profiling, and multi‑objective optimization are applied to news personalization at Toutiao, highlighting data sampling, machine‑learning pipelines, challenges of news freshness, and architectural evolution.

multi-objective optimizationnews recommendationrecommendation

0 likes · 5 min read