https://www.cikm2022.org/papers-posters

• 工具包：
  https://github.com/RUCAIBox/RecBole
  https://github.com/RUCAIBox/RecBole2.0
• 数据集：
  https://github.com/RUCAIBox/RecSysDatasets
  
  
• 论文（RecBole 2.0已被CIKM 2022录用为Resource Paper）：
  RecBole 2.0: Towards a More Up-to-Date Recommendation Library

1

『目录』

• Click-Through Rate
• Collaborative Filtering
• Sequential/Session-based Recommendation
• Knowledge-Aware Recommendation
• Social Recommendation
• News Recommendation
• Text-Aware Recommendation
• Conversational Recommender System
• Cross-domain Recommendation
• Online Recommendation
• Group Recommendation
• Other Tasks

• Debias in Recommender System
• Fairness in Recommender System
• Explanation in Recommender System
• Cold-start in Recommender System
• Ranking in Recommender System
• Evaluation
• Others

• Graph Neural Network in Recommender System
• Contrastive Learning in Recommender System
• Variational Autoencoder in Recommender System
• Meta/Zero-shot/Few-shot Learning

• Pre-training
• Information Retrieval
• Knowledge Graph

2

『正文』

1. Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models
2. Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
3. Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
4. GRP: A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation
5. Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
6. OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
7. Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction【applied paper】
8. GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction【applied paper】

1. Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation
2. Dynamic Hypergraph Learning for Collaborative Filtering
3. NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution
4. MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies
5. ITSM-GCN: Informative Training Sample Mining for Graph Convolution Network-based Collaborative Filtering

1. Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
2. Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
3. Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
4. Dual-Task Learning for Multi-Behavior Sequential Recommendation
5. Dually Enhanced Propensity Score Estimation in Sequential Recommendation
6. Temporal Contrastive Pre-Training for Sequential Recommendation
7. Storage-saving Transformer for Sequential Recommendations
8. Time Lag Aware Sequential Recommendation
9. Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation
10. A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation【applied paper】

1. Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning
2. Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation
3. Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
4. Knowledge Enhanced Multi-Interest Network for the Generation of Recommendation Candidates【applied paper】

1. User Recommendation in Social Metaverse with VR

1. DeepVT: Deep View-Temporal Interaction Network for News Recommendation

1. Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
2. Improving Text-based Similar Product Recommendation for Dynamic Product Advertising at Yahoo【applied paper】

1. Rethinking Conversational Recommendations: Is Decision Tree All You Need?
2. Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference

1. Contrastive Cross-Domain Sequential Recommendation
2. Cross-domain Recommendation via Adversarial Adaptation
3. Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation
4. FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction
5. Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
6. Adaptive Domain Interest Network for Multi-domain Recommendation【applied paper】

1. Knowledge Extraction and Plugging for Online Recommendation【applied paper】
2. SASNet: Stage-aware sequential matching for online travel recommendation【applied paper】

1. GBERT: Pre-training User representations for Ephemeral Group Recommendation

1. MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation
2. Target Interest Distillation for Multi-Interest Recommendation
3. A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention
4. HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
5. Task Publication Time Recommendation in Spatial Crowdsourcing
6. AutoMARS: Searching to Compress Multi-Modality Recommendation Systems
7. MIC: Model-agnostic Integrated Cross-channel Recommender【applied paper】
8. A Case Study in Educational Recommenders: Recommending Music Partitures at Tomplay【applied paper】
9. Real-time Short Video Recommendation on Mobile Devices【applied paper】
10. Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation【applied paper】
11. Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems【applied paper】

1. Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
2. Representation Matters When Learning From Biased Feedback in Recommendation
3. Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
4. Unbiased Learning to Rank with Biased Continuous Feedback
5. Debiased Balanced Interleaving at Amazon Search【applied paper】
6. Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning【applied paper】

1. RAGUEL: Recourse-Aware Group Unfairness Elimination
2. Towards Principled User-side Recommender Systems

1. Explanation Guided Contrastive Learning for Sequential Recommendation

1. Generative Adversarial Zero-Shot Learning for Cold-Start News Recommendation
2. Addressing Cold Start in Product Search via Empirical Bayes【applied paper】

1. Rank List Sensitivity of Recommender Systems to Interaction Perturbations
2. Memory Bank Augmented Long-tail Sequential Recommendation
3. A Biased Sampling Method for Imbalanced Personalized Ranking

1. KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

1. An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering
2. Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
3. PROPN: Personalized Probabilistic Strategic Parameter Optimization in Recommendations【applied paper】
4. UDM: A Unified Deep Matching Framework in Recommender Systems【applied paper】
5. Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation【applied paper】

1. SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
2. Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
3. Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks
4. Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-video Recommendation
5. The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation
6. PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation【applied paper】

1. Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
2. Domain-Agnostic Constrastive Representations for Learning from Label Proportions
3. Multi-level Contrastive Learning Framework for Sequential Recommendation

1. ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

1. Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation
2. Multimodal Meta-Learning for Cold-Start Sequential Recommendation【applied paper】

1. Cognize Yourself: Graph Pre-Training via Core Graph Cognizing and Differentiating
2. CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
3. Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection
4. Graph Neural Networks Pretraining Through Inherent Supervision for Molecular Property Prediction【applied paper】
5. Fooling MOSS Detection with Pretrained Language Models【applied paper】

1. CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
2. Contrastive Label Correlation Enhanced Unified Hashing Encoder for Cross-modal Retrieval
3. Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
4. Detecting Significant Differences Between Information Retrieval Systems via Generalized Linear Models
5. PLAID: An Efficient Engine for Late Interaction Retrieval
6. Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval
7. SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
8. Dense Retrieval with Entity Views
9. Approximated Doubly Robust Search Relevance Estimation【applied paper】
10. Cross-Domain Product Search with Knowledge Graph【applied paper】

1. Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
2. Explainable Link Prediction in Knowledge Hypergraphs
3. I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
4. Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities
5. Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
6. Contrastive Knowledge Graph Error Detection
7. Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs
8. DA-Net: Distributed Attention Network for Temporal Knowledge Graph Reasoning
9. Discovering Fine-Grained Semantics in Knowledge Graph Relations
10. High-quality Task Division for Large-scale Entity Alignment
11. Interactive Contrastive Learning for Self-Supervised Entity Alignment
12. Cognitive Diagnosis focusing on Knowledge Components【applied paper】

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