SIGIR2020推荐系统论文聚焦
目录
前言
推荐论文列表
前言
第43届国际信息检索研究和发展大会(SIGIR)将于2020年7月25-30日在美丽的中国西安举行。此次大会共收到了555篇长文投稿,录用147篇,长文录取率26.4%;共收到了507篇短文投稿,录用153篇,短文录取率30%。
正因为推荐与搜索是解决信息过载的两种有效途径,因此虽然是关于检索的会议,但通过下图可以看出推荐(Recommendation)占据了很大比例,与搜索(Search+Retrieval)不相上下。另外,图与网络(Graph/Network)数据成为研究的主要对象,毕竟许多待研究的对象都可以表示为图。值得注意的是,神经网络(Neural)仍然排在前列;融合知识(Knowledge)的搜索/推荐系统也被许多研究者研究。除此之外,强化学习也出现在了排行榜中,可见利用强化学习的思想来迭代优化搜索/推荐逐渐成为流行。
另外,还注意到今年SIGIR开办了一场关于对话推荐/检索的Tutorial,感兴趣的小伙伴可以多多关注。想提前了解对话推荐(Conversational RS,CRS)的朋友,可以公众号后台回复【CRS】获取对话推荐系统最新综述。
推荐论文列表
本次只对大会的长文(Full Papers)进行梳理,因此共整理出63篇关于推荐系统的论文。为了方便查看与了解,我们主要将其分为了以下几类:Sequential RS,Graph-based RS,Cold-start in RS,Efficient RS,Knowledge-aware RS,Robust RS,Group RS,Conversational RS,RL for RS,Cross-domain RS,Explainable RS,POI RS。另外,对于有一些不包含在以上类别的文章,我们统一归为了Others。当然,以上分类仁者见仁,智者见智,目的是给大家一个相对清晰的结构。具体的各个类别所包含的论文数见下表。
分类 | 数量 |
---|---|
Sequential RS | 8 |
Graph-based RS | 6 |
Robust RS | 6 |
Efficient RS | 5 |
Knowledge-aware RS | 5 |
Cold-start in RS | 4 |
Group RS | 4 |
Conversational RS | 4 |
RL for RS | 3 |
Cross-domain RS | 2 |
Explainable RS | 2 |
POI RS | 1 |
Others | 13 |
可见,序列化推荐的文章占比较大;随后是基于图的推荐、鲁棒的推荐系统;其次是提升推荐效率的文章、基于知识的推荐以及解决冷启动问题的推荐文章、组推荐、对话推荐系统;最后是强化学习推荐、跨域推荐、可解释推荐以及兴趣点推荐。当然其他类别中也包含了许多有意思的研究,比如消除推荐偏置(Bias)的文章、分布式训练推荐系统的文章以及如何retrain推荐系统的文章等。
接下来是分类好的推荐论文列表,大家可以根据自己的研究子方向进行精读。
Sequential RS
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation.
Sequential Recommendation with Self-attentive Multi-adversarial Network.
A General Network Compression Framework for Sequential Recommender Systems.
Next-item Recommendation with Sequential Hypergraphs.
KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation.
Time Matters: Sequential Recommendation with Complex Temporal Information.
Modeling Personalized Item Frequency Information for Next-basket Recommendation.
Graph-based RS
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach.
Multi-behavior Recommendation with Graph Convolution Networks.
Hierarchical Fashion Graph Network for Personalised Outfit Recommendation.
Neighbor Interaction Aware Graph Convolution Networks for Recommendation.
Disentangled Representations for Graph-based Collaborative Filtering.
Cold-start RS
Content-aware Neural Hashing for Cold-start Recommendation.
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste.
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation.
AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems.
Efficient RS
Lightening Graph Convolution Network for Recommendation.
A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data.
Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.
Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval.
Knowledge-aware RS
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation.
Fairness-Aware Explainable Recommendation over Knowledge Graphs.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View.
Make It a CHORUS: Context- and Knowledge-aware Item Modeling for Recommendation.
CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems.
Robust RS
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Identification.
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines.
DPLCF: Differentially Private Local Collaborative Filtering.
Data Poisoning Attacks against Differentially Private Recommender Systems.
Group RS
GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation.
GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation.
Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation.
Global Context Enhanced Graph Nerual Networks for Session-based Recommendation.
Conversational RS
Deep Critiquing for VAE-based Recommender Systems.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning.
Towards Question-based Recommender Systems.
Neural Interactive Collaborative Filtering.
RL for RS
Self-Supervised Reinforcement Learning for Recommender Systems.
MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations.
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs.
Cross-domain RS
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation.
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network.
Explainable RS
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations.
Try This Instead: Personalized and Interpretable Substitute Recommendation.
POI RS
HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation.
Others
Learning Personalized Risk Preferences for Recommendation.
Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters.
Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems.
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation.
MVIN: Learning multiview items for recommendation.
How to Retrain a Recommender System?
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems.
BiANE: Bipartite Attributed Network Embedding.
ASiNE: Adversarial Signed Network Embedding.
Learning Dynamic Node Representations with Graph Neural Networks.
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback.
推荐阅读
[0].入门推荐系统,这25篇综述文章足够了
[1].评论文本信息对推荐真的有用吗?
[2].IJCAI'20最新推荐系统论文聚焦
[3].MLP or IP:推荐模型到底用哪个更好?
喜欢的话点个在看吧????
SIGIR2020推荐系统论文聚焦相关推荐
- IJCAI'20最新推荐系统论文聚焦
时间过得真快,仿佛昨天刚整理完IJCAI'19最新推荐系统相关论文,今天就要开始整理2020年IJCAI中关于推荐系统的论文了. 首先,我们来说一说今年的IJCAI.好像一直在被吐槽,从一开始的灭霸操 ...
- SIGIR2020推荐系统论文解析:Recommendation for New Users and New Items
冷启动问题的解决方案是推荐系统的一个重要的研究热点.本文解析的论文来自顶会SIGIR2020,论文构建了一个新颖的深度学习模型Heater来对新用户或新物品进行推荐.本文旨在理清顶会论文的思路和框架, ...
- KDD2021推荐系统论文集锦
嘿,记得给"机器学习与推荐算法"添加星标 一年一度的知识发现与数据挖掘顶级会议SIGKDD将于8月14日至18日在线上举行.据统计,今年共有1541篇有效投稿,其中238篇论文被接 ...
- 资源分享 | WSDM2020推荐系统论文打包下载
前言 在今年2月份召开的WSDM是检索和推荐领域的重要会议,虽然只是CCF定义的B类会议,但是却也是推荐方向研究者需要重点关注的.之前分享的一篇关于利用对抗技术来权衡推荐精度与用户隐私的文章就出自于W ...
- 【推荐系统】KDD2021推荐系统论文集锦
嘿,记得给"机器学习与推荐算法"添加星标 一年一度的知识发现与数据挖掘顶级会议SIGKDD将于8月14日至18日在线上举行.据统计,今年共有1541篇有效投稿,其中238篇论文被接 ...
- (ACL+ICML)2020推荐系统相关论文聚焦(附下载链接)
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 ...
- KDD2022推荐系统论文集锦
嘿,记得给"机器学习与推荐算法"添加星标 第28届SIGKDD会议将于8月14日至18日在华盛顿举行.据统计,今年共有1695篇有效投稿,其中254篇论文被接收,接收率为14.98 ...
- KDD2022推荐系统论文集锦(附pdf下载)
嘿,记得给"机器学习与推荐算法"添加星标 第28届SIGKDD会议将于8月14日至18日在华盛顿举行.据统计,今年共有1695篇有效投稿,其中254篇论文被接收,接收率为14.98 ...
- RecSys2021推荐系统论文集锦
嘿,记得给"机器学习与推荐算法"添加星标 第15届推荐系统年会(ACM RecSys 2021)将于9月27日-10月1日在荷兰阿姆斯特丹举行,大会表明可以以更包容的方式通过线上的 ...
最新文章
- Android app开发捷径,让你少去踩坑
- JZOJ 4.1 B组 删数
- 软件工程实践之词频统计
- SQL Server 中的ROWID
- css3抽奖转盘,从零制作CSS3抽奖大转盘
- JAVA学习:maven开发环境快速搭建How to download J2EE API (javaee.jar) from Maven
- WCF 中序列化自定义依赖属性类
- 读《白帽子讲Web安全》之客户端脚本安全(一)
- 机器学习实战7-sklearn集成学习和随机森林
- C++中字符数组和字符串string
- WEB消息提醒实现之二 实现方式-websocket实现方式
- Java Web学习总结(39)——JavaEE常用的Jar详解
- win10 sshsecureshellclient删除profile保存的信息
- boost::asio c++ 网络编程socket通信一个简单例子
- Ajax专题:异步交互局部刷新初步
- 设计模式(1)-- 七大软件设计原则-开闭原则
- NV21数据的镜像算法
- http转socks软件SOCKS2HTTP的使用
- 远程连接工具rdcman
- JS代码简单一段即可破解QQ空间删除说说
热门文章
- 理海大学计算机专业好申吗,美国留学选工科就来了解一下理海大学~
- android 仿小米相机,android-自定义相机遇小米3生成图片花屏
- TCP编程服务器与客户端对话
- 如何用Deep Learning为股票定价
- 购买Blender cloud支援今年官方开源电影Gooseberry
- STM32 软件 I2C Source Files (No Clock Strech)
- WatchGuard Firebox配置动态口令(OTP)认证
- python爬取公众号阅读量_公众号没做起来,那是你菜 | 爬取21个公众号数据后
- 各种EDA软件的PCB文件后缀名
- IntelliJ IDEA(社区版) 背景图片、颜色、字体等设置