MoST-Rec 2019: Workshop on Model Selection and Parameter Tuning in Recommender Systems

Страна: Китай

Город: Beijing

Тезисы до: 08.08.2019

Даты: 04.11.19 — 07.11.19

Область наук: Технические;

Е-мейл Оргкомитета: most-rec@gt-arc.com

Организаторы: University of Science and Technology of China, China and others

 

List of Topics

MoST-Rec Workshop aims to connect the domains of Recommender Systems (RS), Model Selection (MS) and Parameter Tuning (PT), and to facilitate knowledge exchange between the communities of these research areas. Therefore, we are looking for topics that explain methods, challenges and insights at the intersection of these domains. In particular, topics of solicited papers include, but are not limited to:

    Model Selection and Parameter Tuning for Recommender Systems
        Ensemble methods
        Online model selection / ensembles
        Online boosting
        Parameter tuning
        High noise model selection / tuning
        Sparsely labeled model selection / tuning
        Distributing model selection or parameter tuning
    Recommender Systems applying model selection and parameter tuning methods
        Short term temporal dynamics (Item popularity, trends)
        Long term temporal dynamics (user tastes)
        Continously changing sets of users and items
        Scenarios with sparse rewards
        Tuning of robustness, convergence, lerning-rate
        Considerations of popularity bias (in the evaluation metrics, learning procedure)

 

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