大家好，我是對白。
ACM SIGIR 2022是CCF A類會議，人工智能領域智能信息檢索（ Information Retrieval，IR）方向最權威的國際會議。會議專注於信息的存儲、檢索和傳播等各個方面，包括研究戰略、輸出方案和系統評估等等。第45屆國際計算機學會信息檢索大會（The 45rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022）計划於今年7月11日-7月15日在西班牙馬德里召開。這次會議共收到794篇長文和667篇短文投稿，有161篇長文和165篇短文被錄用，錄用率約為20%和24.7%。官方發布的接收論文列表：
Accepted Paperssigir.org/sigir2022/program/accepted/
本文選取了SIGIR 2022中170篇長文或短文，**重點對推薦系統相關論文（124篇）按不同的任務場景和研究話題進行分類整理，也對其他熱門研究方向（問答、對話、知識圖譜等，46篇）進行了歸類**，以供參考。文章也同步發布在**AI** **Box**知乎專欄（知乎搜索「 AI Box專欄」），整理過程中難免有疏漏，歡迎大家在知乎專欄的文章下方評論留言，交流探討！
從詞雲圖看**今年SIGIR的研究熱點**：根據長文和短文的標題繪制如下詞雲圖，可以看到今年研究方向依舊集中在Recommendation，也包括Retrieval、Query等方向；主要任務包括：Ranking、Cross-domain、Multi-Model/Behavior、Few-Shot、User modeling、Conversation等；熱門技術包括：Neural Networks、Knowledge Graph、GNN、Contrastive Learning、Transformer等，其中基於Graph的方法依舊是今年的研究熱點。
**本文目錄**
--------
**1 按照任務場景划分**
* CTR
* Collaborative Filtering
* Sequential/Session-based Recommendation
* Conversational Recommender System
* POI Recommendation
* Cross-domain/Multi-behavior Recommendation
* Knowledge-aware Recommendation
* News Recommendation
* Others
**2 按照主要技術划分**
* GNN-based
* RL-based
* Contrastive Learning based
* AutoML-based
* Others
**3 按照研究話題划分**
* Bias/Debias in Recommender System
* Explanation in Recommender System
* Long-tail/Cold-start in Recommender System
* Fairness in Recommender System
* Diversity in Recommender System
* Attack/Denoise in Recommender System
* Others
**4 其他研究方向**
* QA
* Knowledge Graph
* Conversation/ Dialog
* Summarization
* Multi-Modality
* Generation
* Representation Learning
* * *
**1.按照任務場景划分**
--------------
### **1.1 CTR /CVR Prediction**
1. Enhancing CTR Prediction with Context-Aware Feature Representation Learning 【上下文相關的特征表示】
2. HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction 【層次化意圖嵌入網絡】
3. NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的網絡結構搜索】
4. NMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering 【模型無關的歸納式協同過濾模塊】
5. Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer 【圖遮蓋的Transformer】
6. Neural Statistics for Click-Through Rate Prediction 【short paper，神經統計學】
7. Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction 【short paper，基於排序的CTR預估】
8. DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction 【基於圖的解耦表示】
9. Deep Multi-Representational Item Network for CTR Prediction 【short paper，多重表示商品網絡】
10. Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction 【short paper，多分辨率小波分析】
11. MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios 【short paper，小規模推薦場景下的元學習】
12. Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction 【short paper，對抗過濾建模用戶長期行為序列】
13. Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction 【short paper，長序列數據集基於聚類的行為采樣】
14. CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper 【short paper，新穎度輔助任務】
### **1.2 Collaborative Filtering**
1. Geometric Disentangled Collaborative Filtering 【幾何解耦的協同過濾】
2. Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超圖上的對比學習】
3. Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering 【圖協同過濾在准確度和新穎度上的表現】
4. Unify Local and Global Information for Top-N Recommendation 【綜合局部和全局信息】
5. Enhancing Top-N Item Recommendations by Peer Collaboration 【short paper ，同齡人協同】
6. Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering 【short paper】
### **1.3 Sequential/Session-based Recommendations**
1. Decoupled Side Information Fusion for Sequential Recommendation 【融合邊緣特征的序列推薦】
2. On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation 【自監督知識蒸餾】
3. Multi-Agent RL-based Information Selection Model for Sequential Recommendation 【多智能體信息選擇】
4. An Attribute-Driven Mirroring Graph Network for Session-based Recommendation 【特征驅動的反射圖網絡】
5. When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation 【多粒度網絡】
6. Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation 【考慮價格和興趣的推薦】
7. AutoGSR: Neural Architecture Search for Graph-based Session Recommendation 【面向圖會話推薦的網絡結構搜索】
8. Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation 【數據分布自適應排序】
9. Multi-Faceted Global Item Relation Learning for Session-Based Recommendation 【多面全局商品關系學習】
10. ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping 【考慮重復消費的網絡】
11. Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation 【基於DPP的損失函數】
12. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation 【建模隱式反饋】
13. Coarse-to-Fine Sparse Sequential Recommendation 【short paper，粗到細的稀疏序列化推薦】
14. Dual Contrastive Network for Sequential Recommendation 【short paper，雙對比網絡】
15. Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism 【short paper， 基於元路徑指導和自注意力機制的可解釋會話推薦】
16. Item-Provider Co-learning for Sequential Recommendation 【short paper，商品-商家一同訓練】
17. RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation 【short paper，融合時間和用戶歷史行為的預訓練模型】
18. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【short paper，意圖解耦增強超圖神經網絡】
19. CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space 【short paper，在一致表示空間上的簡單有效會話推薦】
20. DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation 【short paper， 需求感知的圖神經網絡】
21. Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation 【short paper，使用非對稱位置編碼的自注意力網絡】
22. ELECRec: Training Sequential Recommenders as Discriminators 【short paper，訓練序列推薦模型作為判別器】
23. Exploiting Session Information in BERT-based Session-aware Sequential Recommendation 【short paper，在基於BERT的模型中利用會話信息】
### **1.4 Conversational Recommender System**
1. Learning to Infer User Implicit Preference in Conversational Recommendation 【學習推測用戶隱偏好】
2. User-Centric Conversational Recommendation with Multi-Aspect User Modeling 【多角度用戶建模】
3. Variational Reasoning about User Preferences for Conversational Recommendation 【用戶偏好的變分推理】
4. Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems 【對話推薦中模仿用戶言論】
5. Improving Conversational Recommender Systems via Transformer-based Sequential Modelling【short paper，基於Transformer的序列化建模】
6. Conversational Recommendation via Hierarchical Information Modeling 【short paper，層次化信息建模】
### **1.5 POI Recommendation**
1. Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation 【多任務圖循環網絡】
2. Learning Graph-based Disentangled Representations for Next POI Recommendation 【學習基於圖的解耦表示】
3. GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation 【軌跡圖加強的Transformer】
4. Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network 【short paper，自修正的多模態Transformer】
5. Empowering Next POI Recommendation with Multi-Relational Modeling 【多重關系建模】
### **1.6 Cross-domain/Multi-behavior Recommendation**
1. Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders 【訓練解耦的域適應網絡來利用流行度偏差】
2. DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation 【解耦表示】
3. Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation 【雙重適應的強化學習】
4. Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation 【域不變的用戶嵌入】
5. Multi-Behavior Sequential Transformer Recommender 【多行為序列化Transformer】
### **1.7 Knowledge-aware Recommendation**
1. Knowledge Graph Contrastive Learning for Recommendation 【知識圖譜上的對比學習】
2. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System 【多級交叉視圖的對比學習】
3. Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator 【利用反事實生成器緩解假知識】
4. HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation 【層次化知識門控網絡】
5. KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums 【醫療論壇上的知識圖譜增強的推薦】
### **1.8 News Recommendation**
1. ProFairRec: Provider Fairness-aware News Recommendation 【商家公平的新聞推薦】
2. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation 【建模隱式反饋】
3. FUM: Fine-grained and Fast User Modeling for News Recommendation 【short paper，細粒度快速的用戶建模】
4. Is News Recommendation a Sequential Recommendation Task? 【short paper，新聞推薦是序列化推薦嗎】
5. News Recommendation with Candidate-aware User Modeling 【short paper，候選感知的用戶建模】
6. MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation 【short paper，視覺語言學增強的多模態新聞推薦】
### **1.9 others**
1. CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users 【為鄉村用戶提供的旅游推薦】
2. PERD: Personalized Emoji Recommendation with Dynamic User Preference 【short paper，個性化表情推薦】
3. Item Similarity Mining for Multi-Market Recommendation 【short paper，多市場推薦中的商品相似度挖掘】
4. A Content Recommendation Policy for Gaining Subscribers 【short paper，為提升訂閱者的內容推薦策略】
5. Thinking inside The Box: Learning Hypercube Representations for Group Recommendation 【超立方體表示用於組推薦】
**2.按照主要技術划分**
--------------
### **2.1 GNN-based**
1. Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation 【多任務圖循環網絡】
2. An Attribute-Driven Mirroring Graph Network for Session-based Recommendation 【特征驅動的反射圖網絡】
3. Co-clustering Interactions via Attentive Hypergraph Neural Network 【超圖神經網絡聚類交互】
4. Graph Trend Filtering Networks for Recommendation 【圖趨勢過濾網絡】
5. EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems 【short paper，高效的特征泄露修正】
6. DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations 【short paper，雙同質超圖卷積網絡】
7. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【short paper，意圖解耦增強超圖神經網絡】
8. DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation 【short paper， 需求感知的圖神經網絡】
### **2.2 RL-based**
1. Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation 【在線推薦中的稀疏獎勵問題】
2. Multi-Agent RL-based Information Selection Model for Sequential Recommendation 【多智能體信息選擇】
3. Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective 【從提示視角看用於推薦的強化學習】
4. Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation 【雙重適應的強化學習】
5. MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations 【元圖增強的離線策略學習】
6. Value Penalized Q-Learning for Recommender Systems 【short paper，值懲罰的Q-Learning】
7. Revisiting Interactive Recommender System with Reinforcement Learning 【short paper，回顧基於強化學習的交互推薦】
### **2.3 Contrastive Learning based**
1. A Review-aware Graph Contrastive Learning Framework for Recommendation 【考慮評論的圖對比學習】
2. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation 【簡單的圖對比學習方法】
3. Knowledge Graph Contrastive Learning for Recommendation 【知識圖譜上的對比學習】
4. Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超圖上的對比學習】
5. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System 【多級交叉視圖的對比學習】
6. Dual Contrastive Network for Sequential Recommendation 【short paper，雙對比網絡】
7. Improving Micro-video Recommendation via Contrastive Multiple Interests 【short paper，對比多興趣提升短視頻推薦】
8. An MLP-based Algorithm for Efficient Contrastive Graph Recommendations 【short paper，基於MLP的算法實現高效圖對比】
9. Multi-modal Graph Contrastive Learning for Micro-video Recommendation 【short paper，多模態圖對比學習】
10. Towards Results-level Proportionality for Multi-objective Recommender Systems 【short paper，動量對比方法】
11. Socially-aware Dual Contrastive Learning for Cold-Start Recommendation 【short paper，社交感知的雙重對比學習】
### **2.4 AutoML-based Recommender System**
1. Single-shot Embedding Dimension Search in Recommender System 【嵌入維度搜索】
2. AutoLossGen: Automatic Loss Function Generation for Recommender Systems 【自動損失函數生成】
3. NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的網絡結構搜索】
### **2.5 Others**
1. Forest-based Deep Recommender 【深度森林】
2. Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates 【基於元學習的可部署可拓展推薦系統】
**3.按照研究話題划分**
--------------
### **3.1 Bias/Debias in Recommender System**
1. Interpolative Distillation for Unifying Biased and Debiased Recommendation
2. Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders 【訓練解耦的域適應網絡來利用流行度偏差】
3. Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback 【雙邊去偏】
4. Mitigating Consumer Biases in Recommendations with Adversarial Training 【short paper，對抗訓練去偏】
5. Neutralizing Popularity Bias in Recommendation Models 【short paper，中和流行度偏差】
6. DeSCoVeR: Debiased Semantic Context Prior for Venue Recommendation 【short paper，在場所推薦中去除語義上下文先驗】
### **3.2 Explanation in Recommender System**
1. Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations 【使用知識圖譜為推薦生成嶄新的、多樣的解釋】
2. PEVAE: A hierarchical VAE for personalized explainable recommendation. 【利用層次化VAE進行個性化可解釋推薦】
3. Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism 【short paper， 基於元路徑指導和自注意力機制的可解釋會話推薦】
### **3.3 Long-tail/Cold-start in Recommender System**
1. Socially-aware Dual Contrastive Learning for Cold-Start Recommendation 【short paper，社交感知的雙重對比學習】
2. Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation 【short paper，通過融合行為轉換冷啟動用戶】
3. Generative Adversarial Framework for Cold-Start Item Recommendation 【short paper，針對冷啟動商品的生成對抗框架】
4. Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder 【short paper，模型無關的自編碼器提升商品冷啟動推薦】
### **3.4 Fairness in Recommender System**
1. Joint Multisided Exposure Fairness for Recommendation 【綜合考慮多邊的曝光公平性】
2. ProFairRec: Provider Fairness-aware News Recommendation 【商家公平的新聞推薦】
3. CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems 【用戶和商家公平的重排序】
4. Explainable Fairness for Feature-aware Recommender Systems 【考慮特征的推薦系統中的可解釋公平】
5. Selective Fairness in Recommendation via Prompts 【short paper，通過提示保證可選的公平性】
6. Regulating Provider Groups Exposure in Recommendations 【short paper，調整商家組曝光】
### **3.5 Diversity in Recommender System**
1. DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph 【多樣化Web API推薦】
2. Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems 【short paper，定向多樣化】
3. Diversity vs Relevance: a practical multi-objective study in luxury fashion recommendations 【short paper，奢侈品推薦中的多目標研究】
### **3.6 Attack/Denoise in Recommender System**
1. Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering 【數據去噪】
2. Less is More: Reweighting Important Spectral Graph Features for Recommendation 【評估重要的圖譜特征】
3. Denoising Time Cycle Modeling for Recommendation 【short paper，去噪時間循環建模】
4. Adversarial Graph Perturbations for Recommendations at Scale 【short paper，大規模推薦中的對抗圖擾動】
### **3.7Others**
1. Privacy-Preserving Synthetic Data Generation for Recommendation 【隱私保護的仿真數據生成】
2. User-Aware Multi-Interest Learning for Candidate Matching in Recommenders 【使用用戶多興趣學習進行候選匹配】
3. User-controllable Recommendation Against Filter Bubbles 【用戶可控的推薦】
4. Rethinking Correlation-based Item-Item Similarities for Recommender Systems 【short paper，反思基於關系的商品相似度】
5. ReLoop: A Self-Correction Learning Loop for Recommender Systems 【short paper，推薦系統中的自修正循環學習】
6. Towards Results-level Proportionality for Multi-objective Recommender Systems 【short paper，結果均衡的多目標推薦系統】
**4.其他研究方向**
------------
### **4.1 QA**
1. DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection 【雙圖注意力網絡】
2. Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion 【反事實學習】
3. PTAU: Prompt Tuning for Attributing Unanswerable Questions 【提示微調】
4. Conversational Question Answering on Heterogeneous Sources 【異質來源的問答】
5. A Non-Factoid Question-Answering Taxonomy
6. QUASER: Question Answering with Scalable Extractive Rationalization
7. Detecting Frozen Phrases in Open-Domain Question Answering 【short paper 在開放域問答中檢測固定短語】
8. Answering Count Query with Explanatory Evidence 【short paper】
### **4.1 Knowledge Graph**
1. Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion 【多模態知識圖譜補全】
2. Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction 【合並上下文圖和邏輯推理進行歸納式關系預測】
3. Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding【元知識遷移解決歸納式知識圖譜嵌入】
4. Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective 【從信息檢索視角思考知識圖譜補全的評測】
5. Relation-Guided Few-Shot Relational Triple Extraction 【short paper，關系指導的few-shot三元組抽取】
### **4.2 Conversation/ Dialog**
1. Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation 【統一對話理解和生成的預訓練模型】
2. Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy 【主動對話策略的新范式】
3. COSPLAY: Concept Set Guided Personalized Dialogue System 【概念集合指導的個性化對話系統】
4. Understanding User Satisfaction with Task-Oriented Dialogue Systems 【理解用戶滿意度】
5. A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems 【short paper 多任務模型仿真用戶】
6. Task-Oriented Dialogue System as Natural Language Generation 【short paper，自然語言生成的對話系統】
### **4.3 Summarization**
1. HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance
2. V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation
3. Unifying Cross-lingual Summarization and Machine Translation with Compression Rate 【使用壓縮率統一跨語言總結和機器翻譯】
4. ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement 【基於提示的對抗領域自適應】
5. Summarizing Legal Regulatory Documents using Transformers 【short ，使用Transformers總結法律監管文檔】
6. QSG Transformer: Transformer with Query-Attentive Semantic Graph for Query-Focused Summarization 【short paper】
7. MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization 【short paper，多通道圖神經網絡】
8. Lightweight Meta-Learning for Low-Resource Abstractive Summarization 【short paper， 輕量級元學習】
9. Extractive Elementary Discourse Units for Improving Abstractive Summarization 【short paper】
### **4.4 Multi-Modality**
1. Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities
2. Progressive Learning for Image Retrieval with Hybrid-Modality Queries
3. CenterCLIP: Token Clustering for Efficient Text-Video Retrieval
4. Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training
5. CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval
6. Bit-aware Semantic Transformer Hashing for Multi-modal Retrieval
7. Video Moment Retrieval from Text Queries via Single Frame Annotation
8. Multimodal Disentanglement Variational AutoEncoders for Zero-Shot Cross-Modal Retrieval
9. A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection in Multi-modal Code-Mixed Memes
10. Animating Images to transfer CLIP for Video-Text Retrieval 【short paper， 使用CLIP進行視頻-文本檢索】
11. Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization 【short paper】
12. An Efficient Fusion Mechanism for Multimodal Low-resource Setting 【short paper，在低資源下的一種高效融合機制】
### **4.5 Generation**
1. Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation
2. Target-aware Abstractive Related Work Generation with Contrastive Learning 【利用對比學習生成生成相關工作】
3. Generating Clarifying Questions with Web Search Results 【利用Web搜索結果生成清晰問題】
4. Choosing The Right Teammate For Cooperative Text Generation 【short paper 】
### **4.6 Representation Learning**
1. Structure and Semantics Preserving Document Representations 【保留結構和語義的文檔表示】
2. Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
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