(ACL+ICML)2020推荐系统相关论文聚焦(附下载链接)

(ACL+ICML)2020推荐系统相关论文聚焦(附下载链接)_第1张图片

前言

第58届国际计算语言学协会年会(ACL,The Association for Computational Linguistics)将于2020年7月6号-8号线上举行。官网公布了ACL2020的论文收录名单,共计收录778篇论文,本次ACL大会共提交了3429篇论文,长文录取率为25%。作为自然语言处理(NLP)领域的顶会,其中有一些涉及NLP技术的推荐系统论文也会投稿于此。

第37届机器学习国际会议(ICML,International Conference on Machine Learning)将于2020年7月12日至18日线上举行。官网公布了ICML2020的论文收录名单,共计收录1088篇论文,本次ICML大会共提交了4990篇论文,录取率为21.8%。

推荐相关论文列表

本次ACL2020大会共整理出5篇关于推荐系统的论文,其中涉及到2篇对话推荐系统和3篇新闻推荐系统。值得注意的是,3篇新闻推荐系统论文均来自于MSRA谢幸老师团队,太强了(●'◡'●)。不难发现,由于ACL为自然语言处理相关的会议,所以推荐系统的比重较小,另外,接收的推荐系统论文中都涉及NLP相关的技术。

另外,本篇文章还整理出了ICML2020中关于推荐系统的论文4篇。其中涉及GCN、非负矩阵分解等技术。

由于在研究推荐系统的时候,也会用到图神经网络以及知识图谱相关的知识,因此后两部分也整理了相关的论文。

推荐系统-ACL2020

  • Dynamic Online Conversation Recommendation

https://www.aclweb.org/anthology/2020.acl-main.305.pdf

https://github.com/zxshamson/dy-conv-rec

  • Towards Conversational Recommendation over Multi-Type Dialogs

https://www.aclweb.org/anthology/2020.acl-main.98.pdf

https://github.com/PaddlePaddle/models/

  • Fine-grained Interest Matching for Neural News Recommendation

https://www.aclweb.org/anthology/2020.acl-main.77.pdf

  • Graph Neural News Recommendation with Unsupervised Preference Disentanglement

https://www.aclweb.org/anthology/2020.acl-main.392.pdf

  • MIND: A Large-scale Dataset for News Recommendation

https://www.aclweb.org/anthology/2020.acl-main.331.pdf

https://github.com/zxshamson/dy-conv-rec

推荐系统-ICML2020

  • Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

https://arxiv.org/abs/2006.15516.pdf

  • Optimization and Analysis of the pAp@k Metric for Recommender Systems

  • Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

  • Ordinal Non-negative Matrix Factorization for Recommendation

https://arxiv.org/pdf/2006.01034.pdf

网络表示学习&图神经网络相关

  • Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction.

  • Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks.

  • GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media.

  • Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection.

  • Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks.

  • Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases.

  • A Graph Auto-encoder Model of Derivational Morphology.

  • AMR Parsing via Graph-Sequence Iterative Inference.

  • Semantic Graphs for Generating Deep Questions.

  • A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation.

  • Relational Graph Attention Network for Aspect-based Sentiment Analysis.

  • Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks.

  • Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification.

  • Heterogeneous Graph Neural Networks for Extractive Document Summarization.

  • Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification.

  • Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension.

  • Aligned Dual Channel Graph Convolutional Network for Visual Question Answering.

  • Multimodal Neural Graph Memory Networks for Visual Question Answering.

  • Fine-grained Fact Verification with Kernel Graph Attention Network

  • Continuous Graph Neural Networks

知识图谱

  • Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases.

  • Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs.

  • Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction.

  • The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents.

  • Knowledge Graph Embedding Compression.

  • Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding.

  • Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information.

  • SEEK: Segmented Embedding of Knowledge Graphs.

  • SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis.

  • Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings.

  • Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward.

  • A Re-evaluation of Knowledge Graph Completion Methods.

  • ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding.

  • Connecting Embeddings for Knowledge Graph Entity Typing.

  • Low-Dimensional Hyperbolic Knowledge Graph Embeddings.

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