“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。8月24日截止提交，诚邀大家投稿！
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
特刊Viability of Supply Networks and Ecosystems: Lessons Learned From COVID-19 Outbreak 征稿开始，关注新冠疫情下新型供应链问题，12月25日截止投稿！
ICDM 2020 推荐系统的研讨会（Workshop）征稿
简介
“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。
研讨会网站研讨会详细信息：
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https://786121244.github.io/NeuRec-Workshop/
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
关键日期
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
Enquiry
Please contact Dr. Shoujin Wang via [email protected].
链接:
NeuRec Workshop：
https://786121244.github.io/NeuRec-Workshop/
ICDM 2020：http://icdm2020.bigke.org/
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
特刊投稿Special Issue: Viability of Supply Networks and Ecosystems: Lessons Learned From COVID-19 Outbreak
简介
About the special issue
Supply chains (SC) and production systems worldwide have experienced an unprecedented series of shocks caused by the COVID-19 virus outbreak - a new instigator of SC disruption, quite unlike any seen in recent times. The resulting global pandemics and SC collapses yield a series of completely novel decision-making settings for SC researchers and practitioners. This Special Issue is motivated by these new and challenging settings and aims at elaborating on a new notion – SC viability as an intersection of resilience, adaptability and sustainability.
详请说明
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https://techjournals.wixsite.com/techjournals/viability-supply-networks-ecosystem
关键日期
Deadline of Manuscript Submission: 25 December 2020
Final Decision Due: 31 July 2021
Tentative Publication Date: 30 November 2021
Submit here:
https://mc.manuscriptcentral.com/tprs
Topics of interest
The special issue aims to address the following, but not limited to, potential topics:
-Adaptive supply networks
-Collaboration of humanitarian and business logistics for survivability
-Collaboration within SC ecosystems (firms, governments, healthcare) for viability
-Collective behavioral actions for SC viability
-Complex adaptive systems with applications to SC viability
-Dynamic analysis of the SC viability using control and simulation
-Ecological modelling approaches to SC viability
-Framing the SC viability and survivability concepts from the network theory perspective
-Flexible capacity and production systems to ensure long-term viability
-Forecasting the impacts of epidemic outbreaks on the SCs
-Game-theoretic modelling of the SC viability
-Impact and value of digital technologies, Industry 4.0, Big Data analytics, and additive manufacturing on SC viability
-Impact of the epidemic outbreaks on SC performance
-Manufacturing viability through resilience
-Network structures of SC ecosystems and their viability
-Optimization of network redundancy to ensure SC viability
-Re-start and recovery of the SCs after the long-term, global interruptions
-Ripple effect and SC viability
-Ripple effect in the intertwined supply networks
-SC sustainability and epidemic outbreaks
-Supply localization concepts within the global networks
-Survivability of intertwined supply networks
-SC re-design during and after the global epidemic outbreaks
-Viable SC designs: localization, globalization or hybrids?
-Viable production and sourcing strategies
参考文献
[1]https://786121244.github.io/NeuRec-Workshop/
[2]https://techjournals.wixsite.com/techjournals/viability-supply-networks-ecosystem
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