【推荐系统】AAAI2022推荐系统论文集锦
2022年第36届人工智能顶级会议AAAI论文列表已经放出,此次会议共收到9251篇论文提交,其中9020篇论文被审稿。最终录取篇数为1349篇,录取率为可怜的15%。由于境外疫情形势依然严峻,大会将在2月22日到3月1日在线上进行举办。
较之历年接受率来说,今年的录取率可以说是断崖式下跌。下图对2017年至今年的投稿量以及接受率进行了可视化,可以说今年的投稿量之多与接受率之低形成了鲜明的对比。
深度学习技术仍然是比较火热的技术之一;
对图数据的研究依然是大家关注的数据形式之一;
自监督学习、半监督学习、多智能体、表示学习是大家主要使用的学习范式;
机器学习应用如目标检测、文本分类、语义分割等是目前大家比较关注的方向。
完整版清单可从官网下载查看。
https://aaai.org/Conferences/AAAI-22/wp-content/uploads/2021/12/AAAI-22_Accepted_Paper_List_Main_Technical_Track.pdf
1. Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, Jae-Gil Lee
2. DiPS: Differentiable Policy for Sketching in Recommender Systems
Aritra Ghosh, Saayan Mitra, Andrew Lan
3. Low-pass Graph Convolutional Network for Recommendation
4. Online certification of preference-based fairness for personalized recommender systems
Virginie Do, Sam Corbett-Davies, Jamal Atif, Nicolas Usunier
5. Modeling Attrition in Recommender Systems with Departing Bandits
6. A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations
7. Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
8. Multi-view Intent Disentangle Graph Networks for Bundle Recommendation
9. SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation
10. Leaping Through Time with Gradient-based Adaptation for Recommendation
Nuttapong Chairatanakul, Hoang NT, Xin Liu, Tsuyoshi Murata
11. Cross-Task Knowledge Distillation in Multi-Task Recommendation
12. FPAdaMetric: False-positive-aware Adaptive Metric Learning for Session-based Recommendation
13. Offline Interactive Recommendation with Natural-Language Feedback
14. Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
15. Obtaining Calibrated Probabilities with Personalized Ranking Models
Wonbin Kweon, SeongKu Kang, Hwanjo Yu
通过整理发现,此次会议接收的推荐系统相关论文主要涉及基于元学习的推荐系统2篇,序列化推荐5篇,基于强化学习的推荐系统4篇以及冷启动推荐2篇。
往期精彩回顾
适合初学者入门人工智能的路线及资料下载 中国大学慕课《机器学习》(黄海广主讲) 机器学习及深度学习笔记等资料打印 机器学习在线手册 深度学习笔记专辑 《统计学习方法》的代码复现专辑 AI基础下载 本站qq群955171419,加入微信群请扫码: