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HomeWorkshopsNeuRec@ICDM2020: Workshop on Advanced Neural Algorithms and Theories for Recommender Systems
NeuRec@ICDM2020: Workshop on Advanced Neural Algorithms and Theories for Recommender Systems
March 24, 2020
NeuRec 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