大家好，我是对白。
ACM SIGIR 2022是CCF A类会议，人工智能领域智能信息检索（ Information Retrieval，IR）方向最权威的国际会议。会议专注于信息的存储、检索和传播等各个方面，包括研究战略、输出方案和系统评估等等。第45届国际计算机学会信息检索大会（The 45rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022）计划于今年7月11日-7月15日在西班牙马德里召开。这次会议共收到794篇长文和667篇短文投稿，有161篇长文和165篇短文被录用，录用率约为20%和24.7%。官方发布的接收论文列表：
Accepted Paperssigir.org/sigir2022/program/accepted/
本文选取了SIGIR 2022中170篇长文或短文，**重点对推荐系统相关论文（124篇）按不同的任务场景和研究话题进行分类整理，也对其他热门研究方向（问答、对话、知识图谱等，46篇）进行了归类**，以供参考。文章也同步发布在**AI** **Box**知乎专栏（知乎搜索「 AI Box专栏」），整理过程中难免有疏漏，欢迎大家在知乎专栏的文章下方评论留言，交流探讨！
从词云图看**今年SIGIR的研究热点**：根据长文和短文的标题绘制如下词云图，可以看到今年研究方向依旧集中在Recommendation，也包括Retrieval、Query等方向；主要任务包括：Ranking、Cross-domain、Multi-Model/Behavior、Few-Shot、User modeling、Conversation等；热门技术包括：Neural Networks、Knowledge Graph、GNN、Contrastive Learning、Transformer等，其中基于Graph的方法依旧是今年的研究热点。
**本文目录**
--------
**1 按照任务场景划分**
* CTR
* Collaborative Filtering
* Sequential/Session-based Recommendation
* Conversational Recommender System
* POI Recommendation
* Cross-domain/Multi-behavior Recommendation
* Knowledge-aware Recommendation
* News Recommendation
* Others
**2 按照主要技术划分**
* GNN-based
* RL-based
* Contrastive Learning based
* AutoML-based
* Others
**3 按照研究话题划分**
* 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.按照任务场景划分**
--------------
### **1.1 CTR /CVR Prediction**
1. Enhancing CTR Prediction with Context-Aware Feature Representation Learning 【上下文相关的特征表示】
2. HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction 【层次化意图嵌入网络】
3. NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的网络结构搜索】
4. NMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering 【模型无关的归纳式协同过滤模块】
5. Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer 【图遮盖的Transformer】
6. Neural Statistics for Click-Through Rate Prediction 【short paper，神经统计学】
7. Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction 【short paper，基于排序的CTR预估】
8. DisenCTR: Dynamic Graph-based Disentangled Representation for Click-Through Rate Prediction 【基于图的解耦表示】
9. Deep Multi-Representational Item Network for CTR Prediction 【short paper，多重表示商品网络】
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，小规模推荐场景下的元学习】
12. Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction 【short paper，对抗过滤建模用户长期行为序列】
13. Clustering based Behavior Sampling with Long Sequential Data for CTR Prediction 【short paper，长序列数据集基于聚类的行为采样】
14. CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper 【short paper，新颖度辅助任务】
### **1.2 Collaborative Filtering**
1. Geometric Disentangled Collaborative Filtering 【几何解耦的协同过滤】
2. Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超图上的对比学习】
3. Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering 【图协同过滤在准确度和新颖度上的表现】
4. Unify Local and Global Information for Top-N Recommendation 【综合局部和全局信息】
5. Enhancing Top-N Item Recommendations by Peer Collaboration 【short paper ，同龄人协同】
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 【融合边缘特征的序列推荐】
2. On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation 【自监督知识蒸馏】
3. Multi-Agent RL-based Information Selection Model for Sequential Recommendation 【多智能体信息选择】
4. An Attribute-Driven Mirroring Graph Network for Session-based Recommendation 【特征驱动的反射图网络】
5. When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation 【多粒度网络】
6. Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation 【考虑价格和兴趣的推荐】
7. AutoGSR: Neural Architecture Search for Graph-based Session Recommendation 【面向图会话推荐的网络结构搜索】
8. Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation 【数据分布自适应排序】
9. Multi-Faceted Global Item Relation Learning for Session-Based Recommendation 【多面全局商品关系学习】
10. ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping 【考虑重复消费的网络】
11. Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation 【基于DPP的损失函数】
12. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation 【建模隐式反馈】
13. Coarse-to-Fine Sparse Sequential Recommendation 【short paper，粗到细的稀疏序列化推荐】
14. Dual Contrastive Network for Sequential Recommendation 【short paper，双对比网络】
15. Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism 【short paper， 基于元路径指导和自注意力机制的可解释会话推荐】
16. Item-Provider Co-learning for Sequential Recommendation 【short paper，商品-商家一同训练】
17. RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation 【short paper，融合时间和用户历史行为的预训练模型】
18. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【short paper，意图解耦增强超图神经网络】
19. CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space 【short paper，在一致表示空间上的简单有效会话推荐】
20. DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation 【short paper， 需求感知的图神经网络】
21. Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation 【short paper，使用非对称位置编码的自注意力网络】
22. ELECRec: Training Sequential Recommenders as Discriminators 【short paper，训练序列推荐模型作为判别器】
23. Exploiting Session Information in BERT-based Session-aware Sequential Recommendation 【short paper，在基于BERT的模型中利用会话信息】
### **1.4 Conversational Recommender System**
1. Learning to Infer User Implicit Preference in Conversational Recommendation 【学习推测用户隐偏好】
2. User-Centric Conversational Recommendation with Multi-Aspect User Modeling 【多角度用户建模】
3. Variational Reasoning about User Preferences for Conversational Recommendation 【用户偏好的变分推理】
4. Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems 【对话推荐中模仿用户言论】
5. Improving Conversational Recommender Systems via Transformer-based Sequential Modelling【short paper，基于Transformer的序列化建模】
6. Conversational Recommendation via Hierarchical Information Modeling 【short paper，层次化信息建模】
### **1.5 POI Recommendation**
1. Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation 【多任务图循环网络】
2. Learning Graph-based Disentangled Representations for Next POI Recommendation 【学习基于图的解耦表示】
3. GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation 【轨迹图加强的Transformer】
4. Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer Network 【short paper，自修正的多模态Transformer】
5. Empowering Next POI Recommendation with Multi-Relational Modeling 【多重关系建模】
### **1.6 Cross-domain/Multi-behavior Recommendation**
1. Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders 【训练解耦的域适应网络来利用流行度偏差】
2. DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation 【解耦表示】
3. Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation 【双重适应的强化学习】
4. Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation 【域不变的用户嵌入】
5. Multi-Behavior Sequential Transformer Recommender 【多行为序列化Transformer】
### **1.7 Knowledge-aware Recommendation**
1. Knowledge Graph Contrastive Learning for Recommendation 【知识图谱上的对比学习】
2. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System 【多级交叉视图的对比学习】
3. Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator 【利用反事实生成器缓解假知识】
4. HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation 【层次化知识门控网络】
5. KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums 【医疗论坛上的知识图谱增强的推荐】
### **1.8 News Recommendation**
1. ProFairRec: Provider Fairness-aware News Recommendation 【商家公平的新闻推荐】
2. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation 【建模隐式反馈】
3. FUM: Fine-grained and Fast User Modeling for News Recommendation 【short paper，细粒度快速的用户建模】
4. Is News Recommendation a Sequential Recommendation Task? 【short paper，新闻推荐是序列化推荐吗】
5. News Recommendation with Candidate-aware User Modeling 【short paper，候选感知的用户建模】
6. MM-Rec: Visiolinguistic Model Empowered Multimodal News Recommendation 【short paper，视觉语言学增强的多模态新闻推荐】
### **1.9 others**
1. CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users 【为乡村用户提供的旅游推荐】
2. PERD: Personalized Emoji Recommendation with Dynamic User Preference 【short paper，个性化表情推荐】
3. Item Similarity Mining for Multi-Market Recommendation 【short paper，多市场推荐中的商品相似度挖掘】
4. A Content Recommendation Policy for Gaining Subscribers 【short paper，为提升订阅者的内容推荐策略】
5. Thinking inside The Box: Learning Hypercube Representations for Group Recommendation 【超立方体表示用于组推荐】
**2.按照主要技术划分**
--------------
### **2.1 GNN-based**
1. Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation 【多任务图循环网络】
2. An Attribute-Driven Mirroring Graph Network for Session-based Recommendation 【特征驱动的反射图网络】
3. Co-clustering Interactions via Attentive Hypergraph Neural Network 【超图神经网络聚类交互】
4. Graph Trend Filtering Networks for Recommendation 【图趋势过滤网络】
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，双同质超图卷积网络】
7. Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【short paper，意图解耦增强超图神经网络】
8. DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation 【short paper， 需求感知的图神经网络】
### **2.2 RL-based**
1. Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation 【在线推荐中的稀疏奖励问题】
2. Multi-Agent RL-based Information Selection Model for Sequential Recommendation 【多智能体信息选择】
3. Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective 【从提示视角看用于推荐的强化学习】
4. Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation 【双重适应的强化学习】
5. MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations 【元图增强的离线策略学习】
6. Value Penalized Q-Learning for Recommender Systems 【short paper，值惩罚的Q-Learning】
7. Revisiting Interactive Recommender System with Reinforcement Learning 【short paper，回顾基于强化学习的交互推荐】
### **2.3 Contrastive Learning based**
1. A Review-aware Graph Contrastive Learning Framework for Recommendation 【考虑评论的图对比学习】
2. Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation 【简单的图对比学习方法】
3. Knowledge Graph Contrastive Learning for Recommendation 【知识图谱上的对比学习】
4. Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超图上的对比学习】
5. Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System 【多级交叉视图的对比学习】
6. Dual Contrastive Network for Sequential Recommendation 【short paper，双对比网络】
7. Improving Micro-video Recommendation via Contrastive Multiple Interests 【short paper，对比多兴趣提升短视频推荐】
8. An MLP-based Algorithm for Efficient Contrastive Graph Recommendations 【short paper，基于MLP的算法实现高效图对比】
9. Multi-modal Graph Contrastive Learning for Micro-video Recommendation 【short paper，多模态图对比学习】
10. Towards Results-level Proportionality for Multi-objective Recommender Systems 【short paper，动量对比方法】
11. Socially-aware Dual Contrastive Learning for Cold-Start Recommendation 【short paper，社交感知的双重对比学习】
### **2.4 AutoML-based Recommender System**
1. Single-shot Embedding Dimension Search in Recommender System 【嵌入维度搜索】
2. AutoLossGen: Automatic Loss Function Generation for Recommender Systems 【自动损失函数生成】
3. NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的网络结构搜索】
### **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 【基于元学习的可部署可拓展推荐系统】
**3.按照研究话题划分**
--------------
### **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 【训练解耦的域适应网络来利用流行度偏差】
3. Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback 【双边去偏】
4. Mitigating Consumer Biases in Recommendations with Adversarial Training 【short paper，对抗训练去偏】
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，在场所推荐中去除语义上下文先验】
### **3.2 Explanation in Recommender System**
1. Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations 【使用知识图谱为推荐生成崭新的、多样的解释】
2. PEVAE: A hierarchical VAE for personalized explainable recommendation. 【利用层次化VAE进行个性化可解释推荐】
3. Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism 【short paper， 基于元路径指导和自注意力机制的可解释会话推荐】
### **3.3 Long-tail/Cold-start in Recommender System**
1. Socially-aware Dual Contrastive Learning for Cold-Start Recommendation 【short paper，社交感知的双重对比学习】
2. Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation 【short paper，通过融合行为转换冷启动用户】
3. Generative Adversarial Framework for Cold-Start Item Recommendation 【short paper，针对冷启动商品的生成对抗框架】
4. Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder 【short paper，模型无关的自编码器提升商品冷启动推荐】
### **3.4 Fairness in Recommender System**
1. Joint Multisided Exposure Fairness for Recommendation 【综合考虑多边的曝光公平性】
2. ProFairRec: Provider Fairness-aware News Recommendation 【商家公平的新闻推荐】
3. CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems 【用户和商家公平的重排序】
4. Explainable Fairness for Feature-aware Recommender Systems 【考虑特征的推荐系统中的可解释公平】
5. Selective Fairness in Recommendation via Prompts 【short paper，通过提示保证可选的公平性】
6. Regulating Provider Groups Exposure in Recommendations 【short paper，调整商家组曝光】
### **3.5 Diversity in Recommender System**
1. DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph 【多样化Web API推荐】
2. Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems 【short paper，定向多样化】
3. Diversity vs Relevance: a practical multi-objective study in luxury fashion recommendations 【short paper，奢侈品推荐中的多目标研究】
### **3.6 Attack/Denoise in Recommender System**
1. Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering 【数据去噪】
2. Less is More: Reweighting Important Spectral Graph Features for Recommendation 【评估重要的图谱特征】
3. Denoising Time Cycle Modeling for Recommendation 【short paper，去噪时间循环建模】
4. Adversarial Graph Perturbations for Recommendations at Scale 【short paper，大规模推荐中的对抗图扰动】
### **3.7Others**
1. Privacy-Preserving Synthetic Data Generation for Recommendation 【隐私保护的仿真数据生成】
2. User-Aware Multi-Interest Learning for Candidate Matching in Recommenders 【使用用户多兴趣学习进行候选匹配】
3. User-controllable Recommendation Against Filter Bubbles 【用户可控的推荐】
4. Rethinking Correlation-based Item-Item Similarities for Recommender Systems 【short paper，反思基于关系的商品相似度】
5. ReLoop: A Self-Correction Learning Loop for Recommender Systems 【short paper，推荐系统中的自修正循环学习】
6. Towards Results-level Proportionality for Multi-objective Recommender Systems 【short paper，结果均衡的多目标推荐系统】
**4.其他研究方向**
------------
### **4.1 QA**
1. DGQAN: Dual Graph Question-Answer Attention Networks for Answer Selection 【双图注意力网络】
2. Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion 【反事实学习】
3. PTAU: Prompt Tuning for Attributing Unanswerable Questions 【提示微调】
4. Conversational Question Answering on Heterogeneous Sources 【异质来源的问答】
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 在开放域问答中检测固定短语】
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 【多模态知识图谱补全】
2. Incorporating Context Graph with Logical Reasoning for Inductive Relation Prediction 【合并上下文图和逻辑推理进行归纳式关系预测】
3. Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding【元知识迁移解决归纳式知识图谱嵌入】
4. Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective 【从信息检索视角思考知识图谱补全的评测】
5. Relation-Guided Few-Shot Relational Triple Extraction 【short paper，关系指导的few-shot三元组抽取】
### **4.2 Conversation/ Dialog**
1. Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation 【统一对话理解和生成的预训练模型】
2. Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy 【主动对话策略的新范式】
3. COSPLAY: Concept Set Guided Personalized Dialogue System 【概念集合指导的个性化对话系统】
4. Understanding User Satisfaction with Task-Oriented Dialogue Systems 【理解用户满意度】
5. A Multi-Task Based Neural Model to Simulate Users in Goal Oriented Dialogue Systems 【short paper 多任务模型仿真用户】
6. Task-Oriented Dialogue System as Natural Language Generation 【short paper，自然语言生成的对话系统】
### **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 【使用压缩率统一跨语言总结和机器翻译】
4. ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement 【基于提示的对抗领域自适应】
5. Summarizing Legal Regulatory Documents using Transformers 【short ，使用Transformers总结法律监管文档】
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，多通道图神经网络】
8. Lightweight Meta-Learning for Low-Resource Abstractive Summarization 【short paper， 轻量级元学习】
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进行视频-文本检索】
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，在低资源下的一种高效融合机制】
### **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 【利用对比学习生成生成相关工作】
3. Generating Clarifying Questions with Web Search Results 【利用Web搜索结果生成清晰问题】
4. Choosing The Right Teammate For Cooperative Text Generation 【short paper 】
### **4.6 Representation Learning**
1. Structure and Semantics Preserving Document Representations 【保留结构和语义的文档表示】
2. Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
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