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RecSys_2021_recommendation_papers.md

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Catalog (目录)

1 Sort by task (按照任务场景划分)

2 Sort by main technique (按照主要技术划分)

3 Sort by topic (按研究话题划分)

Sort by task(按照任务场景划分)

CTR /CVR Prediction

  • Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning RecSys 2021 【移动设备的用户的点击行为预测】
  • Page-level Optimization of e-Commerce Item Recommendations RecSys 2021 【商品详细信息页面的商品推荐模块】
  • An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction RecSys 2021 【LBR，点击后转换率预测的全空间多任务模型分析】

Collaborative Filtering

  • Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All RecSys 2021【矩阵分解式协同过滤的理论联系】
  • Negative Interactions for Improved Collaborative-Filtering: Don’t go Deeper, go Higher RecSys 2021【线性全秩模型考虑高阶交互的扩展】
  • Privacy Preserving Collaborative Filtering by Distributed Mediation RecSys 2021【隐私保护式协同过滤】
  • ProtoCF: Prototypical Collaborative Filtering for Few-shot Item Recommendation RecSys 2021【少样本物品推荐的原型协同过滤】
  • Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization RecSys 2021 【Reproducibility paper，神经协同过滤与矩阵分解的比较】
  • Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative Filtering RecSys 2021【LBR，协同过滤中非负矩阵分解的过参数化研究】

Sequential/Session-based Recommendation

  • Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction RecSys 2021 【序列推荐的黑盒攻击】
  • Next-item Recommendations in Short Sessions RecSys 2021 【短会话中的下一个物品推荐】
  • Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation RecSys 2021 【建立自然语言处理和序列推荐的联系】
  • A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models RecSys 2021 【Reproducibility paper，序列推荐模型评测策略的研究】
  • Sequence Adaptation via Reinforcement Learning in Recommender Systems RecSys 2021 【LBR，用强化学习方法进行序列推荐】

Conversational Recommender System

  • Large-scale Interactive Conversational Recommendation System RecSys 2021 【大规模交互式对话推荐系统】
  • Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation RecSys 2021 【将强化学习用于基于对话框的交互式推荐任务】
  • Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison RecSys 2021 【Reproducibility paper，对话推荐中基于生成和基于检索的方法对比】
  • Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions RecSys 2021 【LBR，对话推荐系统的偏好启发方法】

Knowledge-aware Recommendation

  • Sparse Feature Factorization for Recommender Systems with Knowledge Graphs RecSys 2021 【利用知识图谱分解推荐系统稀疏特征】

Social Recommendation

  • I want to break free! Recommending friends from outside the echo chamber RecSys 2021 【有回声室意识的朋友推荐方法】
  • The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending RecSys 2021 【针对双回声室效应的社交媒体极化模型】
  • Auditing the Effect of Social Network Recommendations on Polarization in Geometrical Ideological Spaces RecSys 2021 【LBR，社交网络推荐对几何意识形态空间中极化的影响】

Cross-domain Recommendation

  • Towards Source-Aligned Variational Models for Cross-Domain Recommendation RecSys 2021 【变分自动编码器用于跨域推荐】

Online Recommendation

  • Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption RecSys 2021 【实时流平台推荐的动态可用性和重复消费】

Music Recommendation

  • Follow the guides: disentangling human and algorithmic curation in online music consumption RecSys 2021 【在线音乐消费中的人为和算法影响】
  • The role of preference consistency, defaults and musical expertise in users' exploration behavior in a genre exploration recommender RecSys 2021 【用户音乐偏好的行为探索】
  • Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? RecSys 2021 【LBR，音乐推荐物品流行度偏差的性别分析】
  • Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior RecSys 2021 【LBR，儿童音乐行为分析】
  • Optimizing the Selection of Recommendation Carousels with Quantum Computing RecSys 2021【LBR，用量子计算优化音乐歌单推荐】
  • Play It Again, Sam! Recommending Familiar Music in Fresh Ways RecSys 2021【LBR，用巡演推荐重复的音乐】
  • Predicting Music Relistening Behavior Using the ACT-R Framework RecSys 2021【LBR，基于心理学的理论来建模音乐重听行为】
  • Siamese Neural Networks for Content-based Cold-Start Music Recommendation RecSys 2021 【LBR，暹罗神经网络进行音乐冷启动推荐】

Other task

  • Accordion: A Trainable Simulator for Long-Term Interactive Systems RecSys 2021 【长期交互系统的可训练模拟器】
  • Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models RecSys 2021 【结果预测中的信息交互】
  • Local Factor Models for Large-Scale Inductive Recommendation RecSys 2021 【归纳推荐的局部因子模型】
  • Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item? RecSys 2021 【反向最大内部产品搜索】
  • “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface RecSys 2021 【通过多列表推荐界面个性化食品推荐】
  • An Interpretable Recommendation Model for Gerontological Care RecSys 2021【LBR，老年护理的推荐研究】
  • Automatic Collection Creation and Recommendation RecSys 2021【LBR，自动创建和推荐收藏夹】
  • Estimating and Penalizing Preference Shifts in Recommender Systems RecSys 2021【LBR，推荐系统中偏好转移的分析】
  • Global-Local Item Embedding for Temporal Set Prediction RecSys 2021【LBR，时间集预测任务】

Sort by main technique (按照主要技术划分)

Adversarial Learning based

  • The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation Learning RecSys 2021 【LBR，对抗训练对流行度偏差的影响研究】

Reinforcement Learning

  • Designing Online Advertisements via Bandit and Reinforcement Learning RecSys 2021 【通过 bandit 和强化学习方法设计在线广告】
  • Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation RecSys 2021 【将强化学习用于基于对话框的交互式推荐任务】
  • Values of Exploration in Recommender Systems RecSys 2021 【基于强化学习推荐算法的用户探索和指标评测】
  • Sequence Adaptation via Reinforcement Learning in Recommender Systems RecSys 2021 【LBR，用强化学习方法进行序列推荐】

Bandit Algorithm

  • Burst-induced Multi-Armed Bandit for Learning Recommendation RecSys 2021 【触发式多臂老虎机问题】
  • Designing Online Advertisements via Bandit and Reinforcement Learning RecSys 2021 【通过 bandit 和强化学习方法设计在线广告】
  • Pessimistic Reward Models for Off-Policy Learning in Recommendation RecSys 2021 【改进离线推荐中 Bandit 方法奖励建模】
  • Top-K Contextual Bandits with Equity of Exposure RecSys 2021 【曝光感知的 Bandit 算法】

VAE-based

  • Fast Multi-Step Critiquing for VAE-based Recommender Systems RecSys 2021 【基于多模态建模假设的变分自动编码器】
  • Towards Source-Aligned Variational Models for Cross-Domain Recommendation RecSys 2021 【变分自动编码器用于跨域推荐】

Other technique

  • cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models RecSys 2021 【用于推荐模型可扩展训练的高速缓存】
  • Hierarchical Latent Relation Modeling for Collaborative Metric Learning RecSys 2021 【协作度量学习的层次潜在关系建模】
  • Learning An Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems RecSys 2021 【自适应元模型生成器】
  • Learning to Represent Human Motives for Goal-directed Web Browsing RecSys 2021 【引入人类动机的心理学构建浏览框架】
  • Semi-Supervised Visual Representation Learning for Fashion Compatibility RecSys 2021 【搭配时装预测的半监督视觉表征学习】
  • Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations RecSys 2021 【结合图表示和上下文单词表示的混合推荐】
  • Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network RecSys 2021 【通过视觉的交叉注意力张力网络提供服饰搭配推荐】
  • A Constrained Optimization Approach for Calibrated Recommendations RecSys 2021 【LBR，结合精度和校准的约束优化方法】
  • Eigenvalue Perturbation for Item-based Recommender Systems RecSys 2021 【LBR，物品推荐中特征值扰动的分析】
  • Optimizing the Selection of Recommendation Carousels with Quantum Computing RecSys 2021【LBR，用量子计算优化音乐歌单推荐】

Sort by topic (按研究话题划分)

Bias/Debias in Recommender System

  • An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes RecSys 2021 【虚假信息过滤气泡的审计：走出信息茧房并改变近期行为】
  • Debiased Explainable Pairwise Ranking from Implicit Feedback RecSys 2021【去偏的可解释贝叶斯个性化排名】
  • Mitigating Confounding Bias in Recommendation via Information Bottleneck RecSys 2021【信息瓶颈缓解混杂偏倚】
  • User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms RecSys 2021【超精度测量中的算法用户偏差】
  • Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? RecSys 2021 【LBR，音乐推荐物品流行度偏差的性别分析】
  • The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation Learning RecSys 2021 【LBR，对抗训练对流行度偏差的影响研究】
  • Transfer Learning in Collaborative Recommendation for Bias Reduction RecSys 2021 【LBR，减少偏差的迁移学习】

Explanation in Recommender System

  • Debiased Explainable Pairwise Ranking from Implicit Feedback RecSys 2021【去偏的可解释贝叶斯个性化排名】
  • EX3: Explainable Attribute-aware Item-set Recommendations RecSys 2021【基于物品集属性的可解释推荐】
  • Do Users Appreciate Explanations of Recommendations? An Analysis in the Movie Domain RecSys 2021【LBR，电影推荐系统中用户对解释的满意度分析】

Long-tail/Cold-start in Recommender System

  • Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders RecSys 2021 【图自动编码器冷启动相似艺术家排序问题】
  • Shared Neural Item Representations for Completely Cold Start Problem RecSys 2021 【通过共享物品表示解决完全冷启动问题】
  • Siamese Neural Networks for Content-based Cold-Start Music Recommendation RecSys 2021 【LBR，暹罗神经网络进行音乐冷启动推荐】

Diversity in Recommender System

  • Towards Unified Metrics for Accuracy and Diversity for Recommender Systems RecSys 2021 【推荐系统准确性和多样性的统一度量】
  • Dynamic Graph Construction for Improving Diversity of Recommendation RecSys 2021 【LBR，改进推荐系统多样性的动态多样图框架】

Denoising in Recommender System

  • Denoising User-aware Memory Network for Recommendation RecSys 2021 【用户感知记忆网络的去噪】

Attack in Recommender System

  • Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction RecSys 2021 【序列推荐的黑盒攻击】

Privacy Protection in Recommender System

  • Privacy Preserving Collaborative Filtering by Distributed Mediation RecSys 2021【隐私保护式协同过滤】
  • A Payload Optimization Method for Federated Recommender Systems RecSys 2021 【联邦推荐的有效负载优化方法】
  • Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback RecSys 2021【隐式数据的联邦推荐系统】
  • FR-FMSS: Federated Recommendation via Fake Marks and Secret Sharing RecSys 2021【LBR，跨用户联邦推荐框架】
  • Horizontal Cross-Silo Federated Recommender Systems RecSys 2021【LBR，联邦推荐对多类利益相关者的影响研究】

Evaluation of Recommender System

  • Evaluating Off-Policy Evaluation: Sensitivity and Robustness RecSys 2021 【离线评估的可解释性评估】
  • Online Evaluation Methods for the Causal Effect of Recommendations RecSys 2021 【推荐中因果效应的在线评估方法】
  • Towards Unified Metrics for Accuracy and Diversity for Recommender Systems RecSys 2021 【推荐系统准确性和多样性的统一度量】
  • Values of Exploration in Recommender Systems RecSys 2021 【基于强化学习推荐算法的用户探索和指标评测】
  • A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models RecSys 2021 【Reproducibility paper，序列推荐模型评测策略的研究】
  • Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently? RecSys 2021 【LBR，推荐系统评估指标的再思考】