© 作者|范欣妍
机构|中国人民大学高瓴人工智能学院
导师|赵鑫教授
研究方向 | 推荐系统
导读
ACM SIGIR 2021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44届国际计算机学会信息检索大会(The 44rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021)计划于今年7月11日-7月15日以线上会议形式召开。这次会议共收到720篇长文和526篇短文投稿,有151篇长文和145篇短文被录用,录用率约为21%和27%。官方发布的接收论文列表:https://sigir.org/sigir2021/accepted-papers/
本文选取了SIGIR 2021中86篇长文,44篇短文,重点对推荐系统相关论文(87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,43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!
· 1 按推荐的任务场景划分·
· 2 按推荐的研究话题划分·
Debias in Recommender System
· 3 其他研究方向·
QA
01
按推荐任务场景划分
1.1 CTR
1.2 Collaborative Filtering
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution【基于老虎机方法根据环境变化动态调整】
Neural Graph Matching based Collaborative Filtering【基于图匹配网络的CF】
Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization【通过MIM来增强图学习】
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation【集合到集合的排序推荐】
1.3 Sequential/Session-based Recommendations
Category-aware Collaborative Sequential Recommendation【类别感知的SR】
Sequential Recommendation with Graph Convolutional Networks【GCN做SR】
Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration【双重Attention迁移】
Unsupervised Proxy Selection for Session-based Recommender Systems【无监督代理选择方法】
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking【针对非常深的网络的更有效率训练方法】
Counterfactual Data-Augmented Sequential Recommendation【反事实数据增强】
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation【反事实用户序列合成】
Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback【流推荐中反事实奖励修改】
Package Recommendation with Intra- and Inter-Package Attention Networks【考虑包内和包间关系的package推荐】
The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation【对比学习做SR】
Motif-aware Sequential Recommendation【short paper,基于主题的SR】
Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation【short paper,通过低秩分解自注意力网络,进行模型轻量化】
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer【short paper】
ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation【short paper】
Temporal Augmented Graph Neural Networks for Session-Based Recommendations【short paper,时序增强的GNN做SR】
1.4 Conversational Recommender System
1.5 News Recommendations
Personalized News Recommendation with Knowledge-aware News Interactions【引入新闻交互知识做个性化新闻推荐】
Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation【知识剪枝与递归图卷积】
1.6 Cross-domain/Multi-behavior Recommendations
Federated Collaborative Transfer for Cross-Domain Recommendation【跨领域推荐中的联合协同迁移】
Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation【通过领域语义和跨领域关联做论文推荐】
Graph Meta Network for Multi-Behavior Recommendation with Interaction Heterogeneity and Diversity【基于图元网络的多行为推荐】
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users【short paper,跨领域推荐中的冷启动】
1.7 Social Recommendation
Social Recommendation with Implicit Social Influence【short paper】
ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation【short paper,通过一致用户增强的GNN】
1.8 基于热门技术
Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems【结构化GCN】
Self-supervised Graph Learning for Recommendation【推荐中的自监督图学习】
DEKR: Description Enhanced Knowledge Graph for Machine Learning Method Recommendation【增强知识图谱】
1.8.2 VAE-based
Variational Autoencoders for Top-K Recommendation with Implicit Feedback【short paper,VAE对Top-K推荐】
Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems【short paper,基于关键词激活向量的贝叶斯评判】
Info-flow Enhanced GANs for Recommender【short paper】
xLightFM: Extremely Memory-Efficient Factorization Machine【高效Memory的因子分解机模型】
1.9 Other Tasks
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue【反事实推荐中减轻点击诱饵的问题】
UGRec: Modeling Directed and Undirected Relations for Recommendation【在推荐中建立有向和无向关系】
Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement【软目标增强的方法】
PreSizE: Predicting Size in E-Commerce using Transformers【使用Transformer预测size(尺码?)】
Did you buy it already? Detecting Users Purchase-State From Their Product-Related Questions【通过产品相关问题检测用户购买状态,新颖性推荐】
Path-based Deep Network for Candidate Item Matching in Recommenders【基于路径的深度网络做候选item匹配】
How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode【基于Taocode的购买预测分析】
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images【可视化推荐】
02
按推荐研究话题划分
2.1 Debias in Recommender System
2.2 Fairness in Recommender System
2.3 Explanation in Recommender System
2.4 Long-tail/Cold-start in Recommender System
2.5 Attack in Recommender System
Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training【以牙还牙,用有毒的对抗网络增强模型鲁棒性】
2.6 Diversity in Recommender System
Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation【从领域层面和用户层面增强推荐多样性】
2.7 Ranking in Recommender System
New Insights into Metric Optimization for Ranking-based Recommendation【排序类推荐模型中的指标优化】
2.8 Evaluation
Standing in Your Shoes: External Assessments for Personalized Recommender Systems【外部评估】
Evaluation measures based on preference graphs【基于偏好图的评估】
2.9 Others
A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning【替代损失学习】
Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization【short paper】
Underestimation Refinement: A General Enhancement Strategy for Exploration in Recommendation Systems【short paper,推荐中的探索问题】
Cross-Batch Negative Sampling for Training Two-Tower Recommenders【short paper,双塔模型中的负采样问题】
03
其他研究方向
3.1 QA
3.2 Conversations
Initiative-Aware Self-Supervised learning for Knowledge-Grounded Conversations【自监督学习对知识型对话】
Conversations Powered by Cross-Lingual Knowledge【跨语言知识】
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots【个性化回复的选择很重要】
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles【针对用户画像建立个性化对话机器人】
3.3 Summarization
Multi-Modal Supplementary-Complementary Summarization using Multi-Objective Optimization【多模型下的补充摘要】
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization【指针网络,多视频摘要】
Transformer Reasoning Network for Personalized Review Summarization【基于Transformer推理网络的评论摘要】
Leveraging Lead Bias for Zero-shot Abstractive News Summarization【zero-shot的新闻摘要】
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization【short paper,在电商领域标题和评论摘要中的文本生成】
Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs【short paper,无监督抽取文本摘要】
3.4 Generation
Multi-type Textual Reasoning for Product-aware Answer Generation【回复生成】
Knowledge-based Review Generation by Coherence Enhanced Text Planning【评论生成】
3.5 Knowledge Graph
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion【few-shot的知识图谱补全】
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion【时序知识图谱补全】
MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion【short paper,one-shot的知识图谱补全】
Knowledge Graph Embedding via Metagraph Learning【short paper,知识图谱嵌入表示】
DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph【short paper,跨领域zero-shot命名实体识别】
3.6 Graph Networks
Graph Similarity Computation via Differentiable Optimal Assignment【图相似度计算】
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method【图卷积中邻居的可靠性】
A Graph-Convolutional Ranking Approach to Leverage the Relational Aspects of User-Generated Content【图卷积排序方法】
Retrieving Complex Tables with Multi-Granular Graph Representation Learning【多粒度图表示学习】
Meta-Inductive Node Classification across Graphs【节点分类】
WGCN: Graph Convolutional Networks with Weighted Structural Features【带权重结构特征的GCN】
3.7 Representation Learning
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning【进化表征学习】
Legal Judgment Prediction with Multi-Stage Case Representation Learning in the Real Court Setting【法院判决里的表征学习】
One Person, One Model, One World: Learning Lifelong User Representation without Forgetting【一个user表示用一生】
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences【short paper,时序网络中的表征学习】
Enhanced Representation Learning for Examination Papers with Hierarchical Document Structure【short paper,分层文档结构中的表示学习】
3.8 Multi-Modality
GilBERT: Generative Vision-Language Pre-Training for Modality-Incomplete Visual-Linguistic Tasks【视觉到语言的跨模态】
Hierarchical Cross-Modal Graph Consistency Learning for Video-Text Retrieval【视频-文字检索】
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