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Amap Tech
Amap Tech
Sep 29, 2025 · Artificial Intelligence

How Gaode’s AI‑Powered Route Planner Saves Money and Time During Holiday Travel

Gaode Map introduces three AI‑driven routing features—toll‑free exit recommendation, global faster‑route optimization, and high‑frequency rapid‑route detection—that combine massive traffic data, multi‑objective algorithms, and real‑time prediction to help users save both toll costs and travel time during the National Day travel peak.

AIBig Datamulti-objective
0 likes · 8 min read
How Gaode’s AI‑Powered Route Planner Saves Money and Time During Holiday Travel
Model Perspective
Model Perspective
Aug 19, 2025 · Operations

Six Strategies to Innovate Mathematical Models for Real‑World Decisions

Model innovation thrives on six key strategies—problem transformation, variable reshaping, merging mechanisms with data, expanding objectives, multi‑agent modeling, and solvability design—each linking mathematical tools to real‑world needs to create more realistic, efficient, and robust decision‑support models.

Game TheoryModelingOperations Research
0 likes · 11 min read
Six Strategies to Innovate Mathematical Models for Real‑World Decisions
Qunar Tech Salon
Qunar Tech Salon
Feb 17, 2025 · Artificial Intelligence

Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization

The article details Qunar’s hotel search ranking system evolution, covering the shift from rule‑based sorting to LambdaMart, the adoption of LambdaDNN deep models, multi‑objective MMOE architectures, multi‑scenario integration, extensive feature engineering, and experimental results demonstrating significant offline and online performance gains.

Learning-to-RankRecommendation Systemsdeep-learning
0 likes · 36 min read
Evolution of Qunar Hotel Search Ranking: From LambdaMart to LambdaDNN and Multi‑Objective Optimization
DataFunSummit
DataFunSummit
Feb 7, 2025 · Artificial Intelligence

Fusion Ranking and Multi-Objective Optimization in Recommendation Systems

This article introduces the role of ranking formulas in recommendation systems, compares sequence and value fusion methods, discusses multi‑objective trade‑offs, explains offline parameter search principles, and demonstrates the open‑source ParaDance framework for automated ranking formula optimization.

Parameter Tuningalgorithm engineeringmulti-objective
0 likes · 17 min read
Fusion Ranking and Multi-Objective Optimization in Recommendation Systems
JD Retail Technology
JD Retail Technology
Dec 26, 2024 · Artificial Intelligence

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

MODRL‑TA is a multi‑objective deep reinforcement learning framework that unites independent Q‑learning agents, a cross‑entropy‑based decision‑fusion module, and progressive data‑augmentation to overcome cold‑start and multi‑objective trade‑offs in e‑commerce traffic allocation, delivering up to 18% more impressions, 4% higher CTR and 5% higher CVR in live tests.

Deep Learninge‑commercemulti-objective
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
Model Perspective
Model Perspective
Oct 11, 2024 · Fundamentals

Beyond Maximizing: Exploring Diverse Decision‑Making Perspectives

This article examines how decision makers can move beyond a single "maximization" goal by considering satisficing, risk minimization, multi‑objective optimization, and regret minimization, offering a richer set of viewpoints for tackling complex, uncertain choices.

decision makingmulti-objectiveoptimization
0 likes · 10 min read
Beyond Maximizing: Exploring Diverse Decision‑Making Perspectives
Model Perspective
Model Perspective
Sep 16, 2024 · Operations

Can 0‑1 Knapsack Modeling Turn You Into a Time‑Management Master?

This article applies 0‑1 knapsack and multi‑objective optimization models to illustrate how students and professionals can allocate limited daily hours among competing tasks, using weighted importance and urgency to devise optimal schedules illustrated with real‑world case studies.

0-1 knapsackmulti-objectiveoptimization
0 likes · 7 min read
Can 0‑1 Knapsack Modeling Turn You Into a Time‑Management Master?
DeWu Technology
DeWu Technology
Dec 20, 2023 · Artificial Intelligence

Coarse Ranking in Recommenders: Key Strategies, Metrics & Optimizations

This article systematically reviews the coarse‑ranking stage of recommendation systems, comparing it with recall and fine‑ranking, defining evaluation metrics, detailing sample design, presenting two technical routes, and exploring optimization directions such as dual‑tower models, knowledge distillation, lightweight fully‑connected layers, multi‑objective and multi‑scenario modeling, followed by practical case studies and results.

Evaluation Metricscoarse rankingdual-tower
0 likes · 22 min read
Coarse Ranking in Recommenders: Key Strategies, Metrics & Optimizations
DataFunSummit
DataFunSummit
Oct 31, 2021 · Artificial Intelligence

Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community

This article details how 58 Community evolved its recommendation system from single‑objective click‑rate optimization to a multi‑objective framework that boosts value‑content share, improves user retention, and leverages cross‑domain embeddings and online CEM‑based parameter tuning to achieve significant performance gains.

CEMEmbeddingOnline Optimization
0 likes · 15 min read
Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community
DataFunTalk
DataFunTalk
Jun 4, 2021 · Artificial Intelligence

Advances in Ranking Algorithms for the "Good Goods" Recommendation Scenario

This article presents a comprehensive overview of recent advancements in ranking algorithms for the Good Goods recommendation scenario, covering long‑sequence modeling, category‑retrieval attention, multi‑objective ranking, model structure optimizations, loss functions, and LTR techniques, along with experimental results and practical insights.

LTRModel Optimizationattention
0 likes · 13 min read
Advances in Ranking Algorithms for the "Good Goods" Recommendation Scenario
DataFunTalk
DataFunTalk
Sep 23, 2019 · Artificial Intelligence

Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking

This article explains how UC international feed recommendation tackles goal definition, multi‑objective point estimation using models such as ESMM, DBMTL and MMoE, mixed‑ranking optimization, and cold‑start challenges by leveraging content understanding and feature generalization to improve user satisfaction.

AIcold-startmachine learning
0 likes · 12 min read
Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking