ICDM21 | 深度推荐系统研讨会日程发布！

01

ICDM2021 NeuRec Workshop介绍

第二届“面向推荐系统的神经网络算法及理论”的国际研讨会（NeuRec Workshop）将于2021年12月7日在新西兰奥克兰（线上）与数据挖掘国际会议ICDM 2021（https://icdm2021.auckland.ac.nz/）同步举办。该研讨会将给国际上致力于数据挖掘、机器学习和推荐系统研究与开发的同行，提供一个广泛的、聚焦的、深度的平台来发布并讨论他们的最新研究成果。

研讨会开始时间为北京时间12月7日周二上午九点。

研讨会官方网站：https://neurec21.github.io/

02

日程安排

Date: Tuesday, 7 December 2021

Beijing Time	Event	Chair
9:00 – 9:10am	Welcome and Opening (10’)  Xiuzhen Zhang, Yan Wang	Xiuzhen Zhang, Yan Wang
9:10 – 10:05am	Keynote Speech 1 (55’)  Guandong Xu, University of Technology Sydney, Australia Causal Learning for Recommender Systems	Shoujin Wang
10:05 – 10:45am	Session 1 (15’+15’ + 10’) Joonyoung Yi, Beomsu Kim, and Buru Chang "Embedding Normalization: Significance Preserving Feature Normalization for Click-Through Rate Prediction" (Best Paper Award) Shaina Raza, Syed Raza Bashir, Dora D. Liu, and Usman Naseem "Balanced News Neural Network for a News Recommender System" Shaoliang Zhang, Xuefeng Liu, Jianwei Niu, and Huiyong Li "ContentHE: Content-enhanced Network Embedding for Hashtag Representation"	Qi Zhang
10:45 – 10:55am	Coffee Break (10’) Gather Town 10mins Break
10:55 –11:50am	Keynote Speech 2 (55’)   Fangzhao Wu, Microsoft Research Asia, China Personalized and Responsible News Recommendation	Wenpeng Lu
11:50 –12:30pm	Session 2 (15’+15’+10’) Tobias Koopmann, Konstantin Kobs, Konstantin Herud, and Andreas Hotho "CoBERT: Scientific Collaboration Prediction via Sequential Recommendation" Wenxiong Wu, Yongli Cheng, Hong Jiang, Fang Wang, Xianghao Xu, Dan He, and Jing Yu "IBFM: An Instance-weight Balanced Factorization Machine for Sparse Prediction" Junbeom Kim, Sihyun Jeong, Goeon Park, Kihoon Cha, Ilhyun Suh, and Byungkook Oh "DynaPosGNN: Dynamic-Positional GNN for Next POI Recommendation"	Qi Zhang
12:30 – 12:40pm	Coffee Break (10’) Gather Town 10mins Break
12:40 – 13:35pm	Keynote Speech 3 (55’)   Jian Cao, Shanghai Jiaotong University, China Towards Building a Fair Recommender Systems	Liang Hu
13:35 – 14:15pm	Session 3 (15’ + 15’+10’) Wei Jiang, Fangquan Lin, Jihai Zhang, Cheng Yang, Hanwei Zhang, and Ziqiang Cui "Dynamic Sequential Recommendation: Decoupling User Intent from Temporal Context" (Best Paper Runner Up Award) Vladimir Provalov, Elizaveta Stavinova, and Petr Chunaev "SynEvaRec: A Framework for Evaluating Recommender Systems on Synthetic Data Classes" Domokos Miklós Kelen and András Benczúr "A probabilistic perspective on nearest neighbor for implicit recommendation"	Liang Hu
14:15 – 14:25pm	Coffee Break (10’) Gather Town 10mins Break
14:25 – 15:20pm	Keynote Speach 4 (55’) Dietmar Jannach, University of Klagenfurt, Austria Recommender Systems, McNamara, and the Illusion of Progress	Shoujin Wang
15:20 – 15:55pm	Session 4 (15’ + 10’+10’) Chuanyan Zhang and Xiaoguang Hong "Challenging the Long Tail Recommendation on Heterogeneous Information Network" Alexander Dallmann, Johannes Kohlmann, Daniel Zoller, and Andreas Hotho"Sequential Item Recommendation in the MOBA Game Dota 2" Minghao Chen and Jiale Zheng "Incorporating Adjacent User Modeling into Session-based Recommendation with Graph Neural Networks"	Sunny Verma
15:55 – 16:00pm	Closing Remarks (5’)	Xiuzhen Zhang, Yan Wang

Organizing Committee

General Chairs

Dr. Shoujin Wang, RMIT University, Macquarie University
Dr. Liang Hu, DeepBlue Academy of Sciences
Prof. Xiuzhen Zhang, RMIT University
Prof. Yan Wang, Macquarie University
Prof. Ivor Tsang, University of Technology Sydney

Publicity Chairs

Prof. Wenpeng Lu, Qilu University of Technology

Dr. Guanfeng Liu, Macquarie University

Dr. Sunny Verma, University of Technology Sydney

Program Chairs

Prof. Wei Liu, University of Technology Sydney

Prof. Hongzhi Yin, The University of Queensland

Dr. Qi Zhang, DeepBlue Academy of Sciences

Trend&Controversy Chairs

Prof. Lina Yao, University of New South Wales

Dr. Chongyang Shi, Beijing Institute of Technology

Award Chairs

Dr. Yong Liu, Nanyang Technological University

Dr. Can Wang, Griffith University

Dr. Wenqi Fan, Hong Kong Polytechnic University

Workflow Chairs

Dora D. Liu, DeepBlue Academy of Sciences

Nengjun Zhu, Shanghai University

Web Master

Qian Zhang,  Qilu University of Technology

03

特邀报告介绍

Keynote Speeches

01

Recommender Systems, McNamara, and the Illusion of Progress

Dietmar Jannach , Professor
University of Klagenfurt, Austria

Abstract:

The field of recommender systems is flourishing. These systems are nowadays used on most major online sites where they can create significant value for both for consumers and providers. In parallel, hundreds of papers are published every year in computer science alone, and most of them report substantial advances to the state-of-the-art with the help of new machine learning algorithms. Recent studies however indicate that progress in this field might in fact be limited due to issues related to methodology and limited reproducibility. In this talk, we review some of these issues and outline possible directions for the future

02

Causal-based Recommender Systems

Guandong Xu, Professor
University of Technology Sydney, Australia

Abstract:

Causal learning has attracted a lot of research attention with the advance in explainable artificial intelligence. Causal learning contains causal discovery and causal inference two directions, where causal inference is to estimate the causal effects in treatment guided by causal graph structure and has been extended in tasks of counterfactual explanation, counterfactual fairness, disentanglement learning, interpretability, and debiasing. In this talk, we will introduce our latest research progress of incorporating causal learning into recommender systems, and present three recent studies on de-biasing confounding in recommendation, causal disentanglement for Intent Learning in Recommendation, and off-policy learning in recommendation. Experimental studies on real world datasets have proven the effectiveness of the proposed models.

03

Towards Building a Fair Recommender Systems

Jian Cao, Professor
Shanghai Jiao Tong University, China

Abstract:

With the wide applications of recommender systems, the potential impacts of recommender systems to the customers, item providers and other parties attract more and more attention. Fairness, which is the quality of treating people equally, is also becoming important in recommender system evaluation and algorithm design. Therefore, in the past years, there has been a growing interest in fairness measurement and assurance in recommender systems. In this talk, the concept of fairness will be discussed in detail in the various contexts of recommender systems. The framework to classify fairness metrics will be proposed from different dimensions. Then the strategies for eliminating unfairness in recommendations will be reviewed. Some research done by the team of the speaker will be presented as case studies. Finally, the challenges and future work will be discussed.

04

Personalized and Responsible News Recommendation

Fangzhao Wu, Senior Researcher
Microsoft Research Asia, China

Abstract:

News Recommendation is critical for handling news information overload and improving users’ online news reading experience. It is widely used in many online news websites and Apps. Compared with traditional recommendation scenarios like e-commerce and movie recommendations, news recommendation has many special characteristics and challenges. In addition, the requirement of responsible news recommender systems becomes higher and higher. In this talk, we will introduce the goal, dataset, and benchmark of news recommendation, as well as the research on personalized and responsible news recommendation.

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