CTRRecommenderModels (ongoing)

1.最新经验总结和前沿研究调研

对学术界和工业界的推荐系统进行了系统性总结，形成了《特征工程》、《召回》和《排序》三个章节，包括技术要点和前沿研究。

1.1 搜广推之《特征工程》前沿论文：

Multi-modal Representation Learning for Short Video Understanding and Recommendation. ICME Workshops 2019.

An Embedding Learning Framework for Numerical Features in CTR Prediction, KDD 2021.

Dynamic Explicit Embedding Representation for Numerical Features in Deep CTR Prediction, CIKM 2022.

Numerical Feature Representation with Hybrid 𝑁 -ary Encoding, CIKM 2022.

AutoFeature: Searching for Feature Interactions and Their Architectures for Click-through Rate Prediction, CIKM 2020.

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction, KDD 2020.

AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction, SIGIR 2020.

Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction, KDD 2023.

AdnFM: An Attentive DenseNet based Factorization Machine for Click-Through-Rate Prediction, ICCDE 2022.

CAN:Feature Co-Action Network for Click-Through Rate Prediction, WSDM 2022.

Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models , DLP-KDD 2021.

FINAL: Factorized Interaction Layer for CTR Prediction, SIGIR 2023.

AdaFS: Adaptive Feature Selection in Deep Recommender System, KDD 2022.

LPFS:Learnable Polarizing Feature Selection for Click-Through Rate Prediction, 2022.

Optimizing Feature Set for Click-Through Rate Prediction, WWW 2023.

Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems, WWW 2023.

Catch: Collaborative Feature Set Search for Automated Feature Engineering, WWW 2023.

经验总结：https://blog.csdn.net/nihaomafb/article/details/133242598

1.2. 推荐系统之《召回》前沿论文

Large Scale Product Graph Construction for Recommendation in E-commerce, 2020.

KGAT: Knowledge Graph Attention Network for Recommendation, KDD 2019.

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall, 2019.

Controllable Multi-Interest Framework for Recommendation, KDD 2019.

Sparse-Interest Network for Sequential Recommendation, WSDM 2021.

Multi-task Learning Model based on Multiple Characteristics and Multiple Interests for CTR prediction, 2022.

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System, CIKM 2019.

Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction, 2020.

End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model, 2021.

Learning Tree-based Deep Model for Recommender Systems, KDD 2019.

DemiNet: Dependency-Aware Multi-Interest Network with Self-Supervised Graph Learning for Click-Through Rate Prediction, AAAI 2022.

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction, ICDE 2022.

Path-based Deep Network for candidate item matching in recommenders, SIGIR 2021.

Sampling-bias-corrected neural modeling for large corpus item recommendations, RS 2019.

经验总结：https://blog.csdn.net/nihaomafb/article/details/133249562

1.3. 推荐系统之《排序》前沿论文：

A Survey on User Behavior Modeling in Recommender Systems, 2023.

Deep interest network for click-through rate prediction, KDD,2018.

DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction, AAAI 2018.

SASRec: Self-attentive Sequential Recommendation, ICDM 2018.

BSTransformer: Behavior Sequence Transformer for E-commerce Recommendation in Alibaba, 2019.

Deep Session Interest Network for Click-Through Rate Prediction, IJCAI 2019.

Learning to Retrieve User Behaviors for Click-through Rate Estimation, TIOS 2023.

A Survey on User Behavior Modeling in Recommender Systems, 2023.

Practice on long sequential user behavior modeling for click-through rate prediction, KDD 2019.

Lifelong sequential modeling with personalized memorization for user response prediction, SIGIR 2019.

Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences, CIKM 2022.

Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction, 2020.

End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model, 2021.

Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction, CIKM 2022.

Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction, SIGIR 2022.

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou, KDD 2023.

Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems from a Multi-task Perspective, WWW 2023.

Denoising Self-Attentive Sequential Recommendation, RS 2022.

Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search, CIKM 2022.

Rethinking Personalized Ranking at Pinterest: An End-to-End Approach, RS 2022.

Page-Wise Personalized Recommendations in an Industrial e-Commerce Setting, RS 2022.

MTBRN: MultiplexTarget-BehaviorRelationEnhancedNetwork forClick-ThroughRatePrediction, CIKM 2020.

Multi-Scale User Behavior Network for Entire Space Multi-Task Learning, CIKM 2022.

Dynamic Multi-Behavior Sequence Modeling for Next Item Recommendation, AAAI 2023.

Hierarchical Projection Enhanced Multi-behavior Recommendation, KDD 2023.

Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit, DASFAA 2021.

Deep Position-wise Interaction Network for CTR Prediction, SIGIR 2021.

AutoDebias: Learning to Debias for Recommendation, SIGIR 2021.

Unbiased Learning to Rank: Online or Offline?, TIOS 2020.

Fair pairwise learning to rank, 2020.

CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems, WWW 2023.

Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction, CIKM 2023.

ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint, 2023.

Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce, SIGIR 2023.

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation, ICDE 2023.

Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction, KDD 2023.

OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction, 2023.

Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling, WSDM 2022.

M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation, KDD 2023.

Automatic Expert Selection for Multi-Scenario and Multi-Task Search, SIGIR 2022.

Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao, WWW 2023.

Cross-domain Augmentation Networks for Click-Through Rate Prediction, 2023.

One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction, CIKM 2021.

HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction, ICDE 2023.

Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems, CIKM 2022.

Large Scale Product Graph Construction for Recommendation in E-commerce, 2020.

Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations, RS 2020.

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations, KDD 2023.

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate, SIGIR 2018.

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising, KDD 2021.

Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey, 2023.

Multi-Objective Recommender Systems: Survey and Challenges, RS 2022.

Optimizing Airbnb Search Journey with Multi-task Learning, KDD 2023.

A Contrastive Sharing Model for Multi-Task Recommendation, WWW 2022.

Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations, 2023.

MSSM: A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning, SIGIR 2021.

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation, 2023.

Multi-Task Recommendations with Reinforcement Learning, WWW 2023.

Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction, SIGIR 2021.

MNCM: Multi-level Network Cascades Model for Multi-Task Learning, CIKM 2022.

Prototype Feature Extraction for Multi-task Learning, WWW 2022.

Fast greedy map inference for determinantal point process to improve recommendation diversity, NIPS 2018.

Neural Re-ranking in Multi-stage Recommender Systems: A Review, 2022.

Generative Flow Network for Listwise Recommendation, KDD 2023.

Context-aware Reranking with Utility Maximization for Recommendation, 2022.

Revisit Recommender System in the Permutation Prospective, 2021.

Entire Cost Enhanced Multi-Task Model for Online-to-Offline Conversion Rate Prediction, 2022.

GRN: Generative Rerank Network for Context-wise Recommendation, 2021.

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation, WWW 2022.

Personalized Diversification for Neural Re-ranking in Recommendation, ICDE 2023.

Multi-Level Interaction Reranking with User Behavior History, SIGIR 2022.

Slate-Aware Ranking for Recommendation, WSDM 2023.

RankFormer: Listwise Learning-to-Rank Using Listwide Labels, kdd 2023.

PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce, KDD 2023.

Multi-factor Sequential Re-ranking with Perception-Aware Diversification, KDD 2023.

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction, NIPS 2022.

AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System, 2022.

NAS-CTR: Efficient Neural Architecture Search for C