CIKM2022推荐系统论文集锦
第31届信息和知识管理国际会议CIKM2022将于2022年10月17日-21日以混合会议的方式在美国亚特兰大举行。CIKM会议是数据库／数据挖掘／内容检索领域顶级国际会议，也是中国计算机学会规定的CCF B类会议。关于该会议在历年推荐系统论文收录情况请参考下文：
本文主要是从教程以及研究型论文和应用型论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、研究型论文59项、应用型论文19项。本次论文整理涉及到众多推荐系统领域的子方向，比如对经典协同过滤方法的改造、序列推荐、智能家居推荐、多模态推荐、大规模推荐问题、跨域推荐、基于图的推荐系统、基于隐私保护的推荐、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
Self-Supervised Learning for Recommendation
Full Papers
本会议所接收的长文主要是关注对经典协同过滤方法的改造、序列推荐、智能家居推荐、多模态推荐、新闻推荐、基于隐私保护的推荐、基于强化学习的推荐系统、基于自监督学习的推荐系统等等。其中大部分论文都已上传到Arxiv，大家可以自行下载进行阅读，也可以前往每周的论文周报进行查看。
| A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention |
| Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge |
| Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation |
| AutoMARS: Searching to Compress Multi-Modality Recommendation Systems |
| Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation |
| Automatic Meta-Path Discovery for Effective Graph-Based Recommendation |
| Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability |
| CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems-CROLoss: 一种推荐系统中检索模型的可定制损失函数 |
| ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation |
| Contrastive Cross-Domain Sequential Recommendation |
| Contrastive Learning with Bidirectional Transformers for Sequential Recommendation |
| Cross-domain Recommendation via Adversarial Adaptation |
| DeepVT: Deep View-Temporal Interaction Network for News Recommendation |
| Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks |
| Dual-Task Learning for Multi-Behavior Sequential Recommendation |
| Dually Enhanced Propensity Score Estimation in Sequential Recommendation |
| Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation |
| Explanation Guided Contrastive Learning for Sequential Recommendation |
| FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction |
| GBERT: Pre-training User representations for Ephemeral Group Recommendation |
| GRP: A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation |
| Generative Adversarial Zero-Shot Learning for Cold-Start News Recommendation |
| Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation |
| Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation |
| HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations |
| Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning |
| KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems |
| Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation |
| MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation |
| Memory Bank Augmented Long-tail Sequential Recommendation |
| Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-video Recommendation |
| Multi-level Contrastive Learning Framework for Sequential Recommendation |
| Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems |
| Rank List Sensitivity of Recommender Systems to Interaction Perturbations |
| Representation Matters When Learning From Biased Feedback in Recommendation |
| Rethinking Conversational Recommendations: Is Decision Tree All You Need? |
| Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation |
| SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation |
| Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks |
| Storage-saving Transformer for Sequential Recommendations |
| Target Interest Distillation for Multi-Interest Recommendation |
| Task Publication Time Recommendation in Spatial Crowdsourcing |
| Temporal Contrastive Pre-Training for Sequential Recommendation |
| The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation |
| Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation |
| Time Lag Aware Sequential Recommendation |
| Towards Principled User-side Recommender Systems |
| Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference |
| User Recommendation in Social Metaverse with VR |
| Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction |
| Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search |
| OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction |
| Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models |
| An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering |
| Dynamic Hypergraph Learning for Collaborative Filtering |
| ITSM-GCN: Informative Training Sample Mining for Graph Convolution Network-based Collaborative Filtering |
| MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies |
| NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution |
| Explainable Link Prediction in Knowledge Hypergraphs |
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，其主要放在了效率和兼容性、大规模推荐场景、可解释性、多样性以及轻量化等提升用户体验的方面。
| A Case Study in Educational Recommenders:Recommending Music Partitures at Tomplay |
| A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation |
| Adaptive Domain Interest Network for Multi-domain Recommendation |
| Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation |
| Improving Text-based Similar Product Recommendation for Dynamic Product Advertising at Yahoo |
| Knowledge Enhanced Multi-Interest Network for the Generation of Recommendation Candidates |
| Knowledge Extraction and Plugging for Online Recommendation |
| MIC:Model-agnostic Integrated Cross-channel Recommender |
| Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems |
| Multimodal Meta-Learning for Cold-Start Sequential Recommendation |
| PROPN: Personalized Probabilistic Strategic Parameter Optimization in Recommendations |
| PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation |
| Real-time Short Video Recommendation on Mobile Devices |
| SASNet: Stage-aware sequential matching for online travel recommendation |
| Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation |
| UDM: A Unified Deep Matching Framework in Recommender Systems |
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推荐系统，是指根据用户的习惯、偏好或兴趣，从不断到来的大规模信息中识别满足用户兴趣的信息的过程。推荐推荐任务中的信息往往称为物品(Item)。根据具体应用背景的不同，这些物品可以是新闻、电影、音乐、广告、商品等各种对象。推荐系统利用电子商务网站向客户提供商品信息和建议，帮助用户决定应该购买什么产品，模拟销售人员帮助客户完成购买过程。个性化推荐是根据用户的兴趣特点和购买行为，向用户推荐用户感兴趣的信息和商品。随着电子商务规模的不断扩大，商品个数和种类快速增长，顾客需要花费大量的时间才能找到自己想买的商品。这种浏览大量无关的信息和产品过程无疑会使淹没在信息过载问题中的消费者不断流失。为了解决这些问题，个性化推荐系统应运而生。个性化推荐系统是建立在海量数据挖掘基础上的一种高级商务智能平台，以帮助电子商务网站为其顾客购物提供完全个性化的决策支持和信息服务。
Arxiv
0+阅读 · 2022年11月27日
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Arxiv
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