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Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1023/a:1007379606734openalex-snapshot

Multitask Learning

inferredcites →https://doi.org/10.1023/a:1018046112532openalex-snapshot

Stacked Regressions

inferredcites →https://doi.org/10.1109/cvpr.2016.433openalex-snapshot

Cross-Stitch Networks for Multi-task Learning

inferredcites →https://doi.org/10.1109/cvpr.2018.00781openalex-snapshot

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

inferredcites →https://doi.org/10.1145/1273496.1273513openalex-snapshot

Learning to rank

inferredcites →https://doi.org/10.1145/1645953.1646169openalex-snapshot

Multi-task learning for learning to rank in web search

inferredcites →https://doi.org/10.1145/1835449.1835676openalex-snapshot

Learning to rank for information retrieval

inferredcites →https://doi.org/10.1145/3077136.3080838openalex-snapshot

On Application of Learning to Rank for E-Commerce Search

inferredcites →https://doi.org/10.1145/3178876.3186165openalex-snapshot

Learning to Collaborate

inferredcites →https://doi.org/10.1145/3209978.3209993openalex-snapshot

Turning Clicks into Purchases

inferredcites →https://doi.org/10.1145/3209978.3210104openalex-snapshot

Entire Space Multi-Task Model

inferredcites →https://doi.org/10.1145/3219819.3219828openalex-snapshot

Perceive Your Users in Depth

inferredcites →https://doi.org/10.1145/3219819.3220007openalex-snapshot

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

inferredcites →https://doi.org/10.1145/3292500.3330658openalex-snapshot

Applying Deep Learning to Airbnb Search

inferredcites →https://doi.org/10.1145/3298689.3347002openalex-snapshot

Addressing delayed feedback for continuous training with neural networks in CTR prediction

inferredcites →https://doi.org/10.1162/neco.1991.3.1.79openalex-snapshot

Adaptive Mixtures of Local Experts

inferredcites →https://doi.org/10.18653/v1/p17-1001openalex-snapshot

Adversarial Multi-task Learning for Text Classification

inferredcites →https://doi.org/10.24963/ijcai.2017/239openalex-snapshot

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.48550/arxiv.1706.05098openalex-snapshot

An Overview of Multi-Task Learning in Deep Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction

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Distillation-Based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation

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Towards Scenario-Adaptive User Behavior Modeling for Multi-scenario Recommendation

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When Multi-scenario Meets Multi-attribute: Scenario and Attribute-Aware Recommendation with Contrastive Learning

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Distributed Meta-learning for Large-Scale Multi-institution Credit Default Risk Prediction

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Multi-view improved sequence behavior with adaptive multi-task learning in ranking

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Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives

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Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation

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Meta Domain Adaptation Approach for Multi-Domain Ranking

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Adversarial Promotion for Video based Recommender Systems

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Enhancing Multi-Task Models For Recommendation with Tensor Trace Norm

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HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction

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ALT: An Automatic System for Long Tail Scenario Modeling

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DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

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BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service

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Adaptive Data and Task Joint Scheduling for Multi-Task Learning

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MSPR: A Multi-Scenario Player Recommendation Framework for Online Gaming

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M 2 oERank: Multi-Objective Mixture-of-Experts Enhanced Ranking for Satisfaction-Oriented Web Search

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On Predicting Post-Click Conversion Rate via Counterfactual Inference

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Parallel Inductive Shift Learning Based Recommendation System

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An Adaptive Entire-Space Multi-Scenario Multi-Task Transfer Learning Model for Recommendations

inferred← citeshttps://doi.org/10.1145/3459637.3481941openalex-snapshot

One Model to Serve All

inferred← citeshttps://doi.org/10.1145/3459637.3481953openalex-snapshot

Self-Supervised Learning on Users' Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce

inferred← citeshttps://doi.org/10.1145/3477495.3531942openalex-snapshot

Automatic Expert Selection for Multi-Scenario and Multi-Task Search

inferred← citeshttps://doi.org/10.1145/3477495.3536320openalex-snapshot

DDEN: A Heterogeneous Learning-to-Rank Approach with Deep Debiasing Experts Network

inferred← citeshttps://doi.org/10.1145/3488560.3498401openalex-snapshot

Learning-To-Ensemble by Contextual Rank Aggregation in E-Commerce

inferred← citeshttps://doi.org/10.1145/3511808.3557154openalex-snapshot

Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation

inferred← citeshttps://doi.org/10.1145/3511808.3557644openalex-snapshot

MNCM: Multi-level Network Cascades Model for Multi-Task Learning

inferred← citeshttps://doi.org/10.1145/3539597.3570399openalex-snapshot

A Bird's-eye View of Reranking: From List Level to Page Level

inferred← citeshttps://doi.org/10.1145/3539618.3591736openalex-snapshot

Multi-Scenario Ranking with Adaptive Feature Learning

inferred← citeshttps://doi.org/10.1145/3539618.3591750openalex-snapshot

PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations

inferred← citeshttps://doi.org/10.1145/3539618.3591944openalex-snapshot

ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction

inferred← citeshttps://doi.org/10.1145/3543873.3584659openalex-snapshot

Multi-Source Domain Adaptation via Latent Domain Reconstruction

inferred← citeshttps://doi.org/10.1145/3580305.3599863openalex-snapshot

M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation

inferred← citeshttps://doi.org/10.1145/3580305.3599936openalex-snapshot

Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction

inferred← citeshttps://doi.org/10.1145/3581783.3612391openalex-snapshot

Task-Adversarial Adaptation for Multi-modal Recommendation

inferred← citeshttps://doi.org/10.1145/3583780.3614651openalex-snapshot

3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems

inferred← citeshttps://doi.org/10.1145/3583780.3614794openalex-snapshot

BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network

inferred← citeshttps://doi.org/10.1145/3583780.3614920openalex-snapshot

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking

inferred← citeshttps://doi.org/10.1145/3583780.3614977openalex-snapshot

Multi-domain Recommendation with Embedding Disentangling and Domain Alignment

inferred← citeshttps://doi.org/10.1145/3583780.3615271openalex-snapshot

MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation

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Multi-gate Mixture-of-Contrastive-Experts with Graph-based Gating Mechanism for TV Recommendation

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Bagpipe: Accelerating Deep Recommendation Model Training

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Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation

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IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation

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MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems

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M 3 oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

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Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context

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DDPO: Direct Dual Propensity Optimization for Post-Click Conversion Rate Estimation

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A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce

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MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation

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Multi-Granularity Modeling in Recommendation: from the Multi-Scenario Perspective

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CM: A Cross and Mixture unit for multitask recommendation

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Residual Multi-Task Learner for Applied Ranking

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Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction

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MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction

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Ranking-Aware Unbiased Post-Click Conversion Rate Estimation via AUC Optimization on Entire Exposure Space

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Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation

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Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System

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Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation

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Scenario Shared Instance Modeling for Click-through Rate Prediction

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DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking

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AMS: Adaptive Modality Scheduling for Industrial Multi-Scenario CTR Prediction

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UME: A Unified Meta-Generalization Framework for Cross-Domain ETA

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Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework

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Distribution-aware Re-representations for Multi-Scenario Recommendations

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Inverse Contrastive Representation Learning for Post-Click Conversion Rate Estimation

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Joint Learning and Optimization for Multi-Product Pricing (and Ranking) Under a General Cascade Click Model

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Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation

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Review of machine learning algorithms for recommender systems in E-commerce platforms

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TAST: Task-Aware Sparse Topology Learning for Multi-Task Recommendation

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One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction

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