转载至：微信公众号 蘑菇先生学习记

本文整理了KDD21的Accepted Papers[1]中，工业界在搜索、推荐、广告、nlp上的文章。整理的论文列表比较偏个人口味，选取的方式是根据论文作者列表上看是否是公司主导的，但判断比较偏主观，存在漏掉的可能。整理的方式主要按照公司和方向来划分，排名不计先后顺序。

1. 按照方向分类

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 推荐系统

1.1.1 样本

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 表征学习

  • 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 跨域推荐

  • 阿里：Debiasing Learning based Cross-domain Recommendation

  • 腾讯：Adversarial Feature Translation for Multi-domain Recommendation

    1.1.4 纠偏

  • 阿里：Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

  • 阿里：Debiasing Learning based Cross-domain Recommendation

    1.1.5 图神经网络

  • 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 多任务学习

  • 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 多模态/短视频推荐

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

    1.1.8 知识图谱

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

    1.1.9 推荐系统架构

  • 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 搜索

    1.2.1 向量检索

  • 阿里：Embedding-based Product Retrieval in Taobao Search

    1.2.2 查询/内容理解

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

    1.2.3 概念图谱

  • 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 预训练

  • 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改写/自动补全

  • 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 图神经网络

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

    1.2.7 多模态

  • Google: Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries

  • Facebook：VisRel: Media Search at Scale

    1.2.8 边缘计算

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

    1.2.9 搜索引擎架构

  • 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 广告

    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 预训练

  • 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 命名实体识别

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

    1.4.3 少样本学习

  • 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 摘要

  • 微软：Reinforcing Pretrained Models for Generating Attractive Text Advertisements

    1.4.5 意图识别

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

    1.4.6 多模态

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

    2.按照公司分类

    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