其中本届会议长文共收到投稿1251篇，其中录用论文271篇，录取率约为21.7%；应用长文共有290篇有效投稿，其中69篇论文被接收，接受率为24%；资源型论文共有80篇有效投稿，其中26篇论文被接收，接收率为32.5%；短文共845篇有效投稿，其中177论文被接收，接受率为20.9%。

本文主要是从教程以及上述提到的资源型论文、长文、短文中筛选出与推荐系统有关的论文供大家学习，其中与推荐系统有关的教程1项、资源型文章2项、长文41项、应用型文章11项和短文21项。另外涉及到众多推荐系统领域的子方向，比如经典的协同过滤、会话推荐、冷启动问题、大规模推荐问题、基于图神经网络的推荐系统、基于强化学习的推荐系统、基于自监督学习的推荐系统等。

Tutorials

本会议带来的教程之一为推荐系统中的机器学习公平性问题，具体标题与作者信息如下。

• CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems - Yunqi Li (Rutgers University, USA), Yingqiang Ge (Rutgers University, USA), Yongfeng Zhang (Rutgers University, USA)

Resource Papers

本会议中关于资源型论文主要是两篇，一篇是Robin Burke大牛带来的librec-auto，一篇是赵鑫老师组带来的RecBole。

• librec-auto: A Tool for Recommender Systems Experimentation

• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

Full Papers

本会议所接收的长文主要是关注在经典的协同过滤技术、冷启动问题、序列化推荐、基于强化学习的推荐、基于图神经网络的推荐、基于自监督的推荐。应用的场景包括音乐推荐、POI推荐、短视频推荐、组推荐、社会化推荐、新闻推荐等。

• SimpleX: A Simple and Strong Baseline for Collaborative Filtering

• LT-OCF: Learnable-Time ODE-based Collaborative Filtering

• Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

• Incremental Graph Convolutional Network for Collaborative Filtering

• Top-N Recommendation with Counterfactual User Preference Simulation

• Counterfactual Review-based Recommendation

• Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

• Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems

• Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation

• How Powerful is Graph Convolution for Recommendation?

• CBML: A Cluster-based Meta-learning Model for Session-based Recommendation

• CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation

• Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders

• Enhancing User Interest Modeling with Knowledge-Enriched Itemsets for Sequential Recommendation

• Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

• Extracting Attentive Social Temporal Excitation for Sequential Recommendation

• Learning An End-to-End Structure for Retrieval in Large-Scale Recommendations

• Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation

• Self-Supervised Graph Co-Training for Session-based Recommendation

• Answering POI-recommendation Questions using Tourism Reviews

• Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation

• Generative Inverse Deep Reinforcement Learning for Online Recommendation

• SeeQuery: An Automatic Method for Recommending Translations of Ontology Competency Questions into SPARQL-OWL

• WG4Rec: Modeling Textual Content with Word Graph for News Recommendation

• SNPR: A Serendipity-Oriented Next POI Recommendation Model

• Hyperbolic Hypergraphs for Sequential Recommendation

• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation

• A Knowledge-Aware Recommender with Attention-Enhanced Dynamic Convolutional Network

• Lightweight Self-Attentive Sequential Recommendation

• Expanding Relationship for Cross Domain Recommendation

• Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation

• Popularity-Enhanced News Recommendation with Multi-View Interest Representation

• Social Recommendation with Self-Supervised Metagraph Informax Network

• USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence

• Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

• Disentangling Preference Representations for Recommendation Critiquing with ?-VAE

• Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems

• Cross-Market Product Recommendation

• Counterfactual Explainable Recommendation

• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

• Answering POI-recommendation Questions using Tourism Reviews

Short Papers

本会议的短文基本与长文所关注的问题类似，在此不再赘述。

• Anchor-based Collaborative Filtering for Recommender Systems

• Causally Attentive Collaborative Filtering

• XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering

• Vector-Quantized Autoencoder With Copula for Collaborative Filtering

• Entity-aware Collaborative Relation Network with Knowledge Graph for Recommendation

• Time-Aware Recommender System via Continuous-Time Modeling

• ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation

• Low-dimensional Alignment for Cross-Domain Recommendation

• GLocal-K: Global and Local Kernels for Recommender Systems

• A Formal Analysis of Recommendation Quality of Adversarially-trained Recommenders

• Fully Hyperbolic Graph Convolution Network for Recommendation

• Dual Correction Strategy for Ranking Distillation in Top-N Recommender System

• DeepGroup: Group Recommendation with Implicit Feedback

• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

• Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation

• Review-Aware Neural Recommendation with Cross-Modality Mutual Attention

• Locker: Locally Constrained Self-Attentive Sequential Recommendation

• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation

• Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems

• Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender Systems

• CauSeR: Causal Session-based Recommendations for Handling Popularity Bias

Applied Papers

本会议所接收的应用型文章与研究型文章的关注点不同，其主要是放在了大规模推荐场景、可解释性、多样性、公平性以及轻量化等提升用户体验的方面。

• Explore, Filter and Distill: Distilled Reinforcement Learning in Recommendation

• Dual Learning for Query Generation and Query Selection in Query Feeds Recommendation

• On the Diversity and Explainability of Recommender Systems: A Practical Framework for Enterprise App Recommendation

• One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction

• CausCF: Causal Collaborative Filtering for Recommendation Effect Estimation

• Fulfillment-Time-Aware Personalized Ranking for On-Demand Food Recommendation

• SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios

• You Are What and Where You Are: Graph Enhanced Attention Network for Explainable POI Recommendation

• Self-supervised Learning for Large-scale Item Recommendations

• LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

• Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations