papers.md

WSDM 2022

跨域推荐

RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

https://arxiv.org/pdf/2111.10093.pdf

Personalized Transfer of User Preferences for Cross-domain Recommendation

https://arxiv.org/pdf/2110.11154.pdf

Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning

序列推荐

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

https://arxiv.org/pdf/2110.05730.pdf

S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation

https://arxiv.org/pdf/2107.03813.pdf

Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation

点击率预估

CAN: Feature Co-Action Network for Click-Through Rate Prediction

Triangle Graph Interest Network for Click-through Rate Prediction

Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search

去偏推荐

It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic

https://arxiv.org/pdf/2111.12481.pdf

Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

http://people.tamu.edu/~zhuziwei/pubs/Ziwei_WSDM_2022.pdf

Towards Unbiased and Robust Causal Ranking for Recommender Systems

路径推荐

PLdFe-RR:Personalized Long-distance Fuel-efficient Route Recommendation Based On Historical Trajectory

联邦推荐

PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion

https://arxiv.org/pdf/2110.10926.pdf

基于图结构的推荐

Joint Learning of E-commerce Search and Recommendation with A Unified Graph Neural Network

Profiling the Design Space for Graph Neural Networks based Collaborative Filtering

http://www.shichuan.org/doc/125.pdf

Graph Logic Reasoning for Recommendation and Link Prediction

Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation

https://arxiv.org/pdf/2108.06468.pdf

公平性推荐

Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning

Enumerating Fair Packages for Group Recommendations

https://arxiv.org/pdf/2105.14423.pdf

基于对比学习的推荐

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

基于元学习的推荐

Long Short-Term Temporal Meta-learning in Online Recommendation

https://arxiv.org/pdf/2105.03686.pdf

基于对抗学习的推荐

A Peep into the Future: Adversarial Future Encoding in Recommendation

基于强化学习的推荐

Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation

A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

https://arxiv.org/pdf/2106.06224.pdf

Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning

https://arxiv.org/pdf/2110.15097.pdf

关于数据集

On Sampling Collaborative Filtering Datasets

The Datasets Dilemma: How Much Do We Really Know About Recommendation Datasets?

其他

VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering

Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce

https://arxiv.org/pdf/2110.11072.pdf

Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

https://arxiv.org/pdf/2110.09905.pdf

Supervised Advantage Actor-Critic for Recommender Systems

https://arxiv.org/pdf/2111.03474.pdf

官网接收论文列表地址：

https://www.wsdm-conference.org/2022/accepted-papers/

KDD 2021

1. Categorize by usage

主要挑选了一些笔者比较感兴趣的方向，并整理了对应的文章名称。读者可以大致读一下文章名，判断是否和自己的研究方向或工作方向一致，从中选择感兴趣的文章进行精读。

1.1 Recommendations

1.1.1 Sampling

涉及到采样、负样本等。

• Google: Bootstrapping for Batch Active Sampling
• Google: Bootstrapping Recommendations at Chrome Web Store
• Alibaba：Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling

1.1.2 Representation Learning

• Google: Learning to Embed Categorical Features without Embedding Tables for Recommendation
• 华为：An Embedding Learning Framework for Numerical Features in CTR Prediction
• 腾讯：Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value
• 阿里：Representation Learning for Predicting Customer Orders

1.1.3 Cross-domain recommendation

• 阿里：Debiasing Learning based Cross-domain Recommendation
• 腾讯：Adversarial Feature Translation for Multi-domain Recommendation

1.1.4 Debiasing learning

• 阿里：Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
• 阿里：Debiasing Learning based Cross-domain Recommendation

1.1.5 Graph Neural Network

• 华为：Dual Graph enhanced Embedding Neural Network for CTR Prediction
• 美团：Signed Graph Neural Network with Latent Groups
• 阿里：DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
• 百度：MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

1.1.6 Multi-task learning

• Google：Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
• 美团：Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning for Customer Acquisition
• 百度：MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

1.1.7 Micro-Video recommendations

• 阿里：SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations

1.1.8 Knowledge Graph generation

• Microsoft：Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning

1.1.9 Recommender Infrastruture

• Facebook：Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
• Facebook：Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters
• 阿里，FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters
• 腾讯，Large-Scale Network Embedding in Apache Spark
• Microsoft，On Post-Selection Inference in A/B Testing

1.2 Search

1.2.1 Embedding

• 阿里：Embedding-based Product Retrieval in Taobao Search

1.2.2 Query understanding

• Facebook：Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook

1.2.3 Knowledge Graph

• 阿里巴巴：AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
• 阿里巴巴：AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce

1.2.4 Pretraining

• 百度：Pretrained Language Models for Web-scale Retrieval in Baidu Search
• 微软：Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

1.2.5 Query rewriting and auto-completion

• 微软：Diversity driven Query Rewriting in Search Advertising
• 百度：Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps

1.2.6 Graph Attention

• 百度：HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps

1.2.7 Multitask

• Google: Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
• Facebook：VisRel: Media Search at Scale

1.2.8 Feature interaction

• 阿里：FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

1.2.9 Serice

• 百度：Norm Adjusted Proximity Graph for Fast Inner Product Retrieval
• 百度：JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu

1.3 Ads

这一块文章不是很多，就不细分了。

• Google: Clustering for Private Interest-based Advertising
• 阿里：A Unified Solution to Constrained Bidding in Online Display Advertising
• 阿里：Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
• 阿里：Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
• 阿里：We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

1.4 NLP

1.4.1 Transformer

• 微软：NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
• 阿里：M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining
• 微软：TUTA: Tree-based Transformers for Generally Structured Table Pre-training

1.4.2 Named Entity Recognition

• 微软：Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition

1.4.3 Multi-label learning

• 微软：Generalized Zero-Shot Extreme Multi-label Learning
• 微软：Zero-shot Multi-lingual Interrogative Question Generation for "People Also Ask" at Bing

1.4.4 Attractive

• 微软：Reinforcing Pretrained Models for Generating Attractive Text Advertisements

1.4.5 User Intent classification

• 阿里：MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning

1.4.6 Multi-Modality

• 阿里：M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining

2 Categorize by Company

2.1 Google

• Learning to Embed Categorical Features without Embedding Tables for Recommendation
• NewsEmbed: Modeling News through Pre-trained Document Representations
• Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning
• Bootstrapping for Batch Active Sampling
• Bootstrapping Recommendations at Chrome Web Store
• Clustering for Private Interest-based Advertising
• Dynamic Language Models for Continuously Evolving Content
• Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
• On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition

2.2 Facebook

• Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
• Preference Amplification in Recommender Systems
• Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters
• Network Experimentation at Scale
• Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook
• VisRel: Media Search at Scale
• Balancing Consistency and Disparity in Network Alignment

2.3 Microsoft

• Generalized Zero-Shot Extreme Multi-label Learning
• Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
• NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
• Reinforced Anchor Knowledge Graph Generation for News Recommendation Reasoning
• Table2Charts: Recommending Charts by Learning Shared Table Representations
• TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
• TUTA: Tree-based Transformers for Generally Structured Table Pre-training
• Contextual Bandit Applications in a Customer Support Bot
• Diversity driven Query Rewriting in Search Advertising
• Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
• On Post-Selection Inference in A/B Testing
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
• Reinforcing Pretrained Models for Generating Attractive Text Advertisements
• Zero-shot Multi-lingual Interrogative Question Generation for "People Also Ask" at Bing

2.4 阿里

• A Unified Solution to Constrained Bidding in Online Display Advertising
• AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
• AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce
• Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
• Debiasing Learning based Cross-domain Recommendation
• Device-Cloud Collaborative Learning for Recommendation
• Deep Inclusion Relation-aware Network for User Response Prediction at Fliggy
• DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
• Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
• Embedding-based Product Retrieval in Taobao Search
• Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
• FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
• FleetRec: Large-Scale Recommendation Inference on Hybrid GPU-FPGA Clusters
• Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection
• Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach
• M6: Multi-Modality-to-Multi-Modality Multitask Mega-transformer for Unified Pretraining
• Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach
• MeLL: Large-scale Extensible User Intent Classification for Dialogue Systems with Meta Lifelong Learning
• Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
• Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
• Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling
• Representation Learning for Predicting Customer Orders
• SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations
• We Know What You Want: An Advertising Strategy Recommender System for Online Advertising

2.5 百度

• Norm Adjusted Proximity Graph for Fast Inner Product Retrieval
• Curriculum Meta-Learning for Next POI Recommendation
• Pretrained Language Models for Web-scale Retrieval in Baidu Search
• HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps
• JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu
• Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps
• MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
• SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps
• Talent Demand Forecasting with Attentive Neural Sequential Model

2.6 腾讯

• Why Attentions May Not Be Interpretable?
• Adversarial Feature Translation for Multi-domain Recommendation
• Large-Scale Network Embedding in Apache Spark
• Learn to Expand Audience via Meta Hybrid Experts and Critics
• Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value

2.7 美团

• Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning for Customer Acquisition
• User Consumption Intention Prediction in Meituan
• Signed Graph Neural Network with Latent Groups
• A Deep Learning Method for Route and Time Prediction in Food Delivery Service

2.8 华为

• An Embedding Learning Framework for Numerical Features in CTR Prediction
• Dual Graph enhanced Embedding Neural Network for CTR Prediction
• Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning
• Retrieval & Interaction Machine for Tabular Data Prediction
• A Multi-Graph Attributed Reinforcement Learning Based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem