ICDM 2020 关于推荐系统的研讨会（Workshop）征稿

“推荐系统中的神经网络算法及理论”的国际研讨会将于2020年11月17日在意大利索伦托（暂定）与数据挖掘国际会议ICDM 2020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，讨论他们的最新研究成果。我们将邀请领域内国际知名专家学者作报告。同时，我们面向广大同行征稿，接收的文章将在会上以口头报告的形式发表，并统一编入由IEEE, EI等机构检索的正式发表的会议集ICDMW，录用的优秀论文经扩展（30%以上）后将被推荐至SCI检索的期刊优先发表，投稿为8页IEEE双栏会议格式。诚邀大家投稿！研讨会及征稿启事（Call for Papers）的详细信息请见下文。

研讨会的举办形式将视新冠疫情的发展情况适时作出相应调整，即可能由线下模式改为远程在线模式举办。

请将如下链接复制到浏览器后打开以访问研讨会网站来获取研讨会最新信息：
https://786121244.github.io/NeuRec-Workshop/

研讨会详细信息

News:

The website of NeuRec Workshop 2020 has been set up.

About the NeuRec Workshop 2020

The Workshop on Advanced Neural Algorithms and Theories for Recommender Systems (NeuRec) will be co-located with ICDM 2020 to be held on November 17-20, 2020, c Italy. This workshop provides a more focused, in-depth venue for presentations, discussions and interactions on the area of data mining, machine learning, artificial intelligence and recommender systems.

Call for Papers

A brief introduction

Nowadays, the renaissance of artificial intelligence (AI) has attracted huge attention from every corner of the world. On the one hand, neural algorithms and theories (include shallow and deep ones) have nearly dominated AI development in almost all areas, e.g., natural language processing (NLP),computer vision (CV) and planning and have shown great promise. On the other hand, recommender systems (RS), as one of the most popular and important applications of AI, has been widely planted into our daily lives and has made a huge difference. Naturally, the combination of neural algorithms and theories and recommender systems has been flourishing for years and has shown great potential. In practice, neural models and algorithms have nearly dominated the recommender system research in recent years. Many state-of-the-art recommender systems are built on neural algorithms, especially deep neural algorithms. However, most researchers often only focus on the application of deep neural models to solve the problems in recommender systems, they either ignore the more efficient shallow and light weighted neural models or overlook the fundamental theories behind these neural models, and the intrinsic connections between these theories and the recommender system issues.
This workshop aims to systematically discuss the recent advancements of both shallow and deep neural algorithms for recommender systems from both the application and theoretical perspectives. Particularly, the recent progress achieved in both shallow and deep neural recommender system algorithms together with the related theories will be discussed. Furthermore, both the recent progress achieved in the academia and the industry will be covered.

This workshop solicits the latest and significant contributions on developing and applying neural algorithms and theories for building intelligent recommender systems. Specifically, the workshop solicits papers (max 8 pages plus 2 extra pages) for peer review. The format of the submissions must be in line with the ICDM submissions, namely double-column in IEEE conference format. Furthermore, as in previous years, papers that are not accepted by the main conference will be automatically sent to a workshop selected by the authors when the papers were submitted to the main conference. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.

Relevant topic areas

The workshop invites submissions on all topics of neural algorithms and theories for recommender systems, including but not limited to:
-Deep neural model for recommender systems
-Shallow neural model for recommender systems
-Neural theories particularly for recommender systems
-Theoretical analysis of neural models for recommender systems
-Theoretical analysis for recommender systems
-Data characteristics and complexity analysis in recommender systems
-Non-IID (non-independent and identical distribution) theories and practices for recommender systems
-Auto ML for recommender systems
-Privacy issues in recommender systems
-Recommendations on small data sets
-Complex behaviour modeling and analysis for recommender systems
-Psychology-driven user modeling for recommender systems
-Brain-inspired neural models for recommender systems
-Explainable recommender systems
-Adversarial recommender systems
-Multimodal recommender systems
-Rich-context recommender systems
-Heterogeneous relation modeling in recommender systems
-Visualization in recommender systems
-New evaluation metrics and methods for recommender systems

Important dates

August 24, 2020: Workshop papers submission
September 17, 2020: Notification of workshop papers acceptance to authors
September 24, 2020: Camera-ready deadline and copyright form
November 17, 2020: Workshops date

Organizing Committee

Dr. Shoujin Wang, Macquarie University
Dr. Liang Hu, Shanghai Jiaotong University
Prof. Yan Wang, Macquarie University
Prof. Longbing Cao, University of Technology Sydney
Prof. Ivor Tsang, University of Technology Sydney