Bridging Cognitive Models and Recommender Systems-Reference-Cited by-同舟云学术

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Bridging Cognitive Models and Recommender Systems

Published:2020-03 Issue:2 Volume:12 Page:426-427
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Angulo Cecilio,Falomir Ing. Zoe,Anguita Davide,Agell Núria,Cambria Erik

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

http://link.springer.com/content/pdf/10.1007/s12559-020-09719-3.pdf

Reference11 articles.

1. Olteteanu A-M, Falomir Z, Freksa C. Artificial cognitive systems that can answer human creativity tests: an approach and two case studies. IEEE Transactions on Cognitive and Developmental Systems, in press. 2017. https://doi.org/10.1109/TCDS.2016.2629622.

2. Puigbo J-Y, Pumarola A, Angulo C, Tellez R. Using a cognitive architecture for general-purpose service robot control. Connection Science, Taylor & Francis. 2015. https://doi.org/10.1080/09540091.2014.968093.

3. Costa V, Dellunde P, Falomir Z. The logical style painting classifier based on Horn clauses and explanations (l-SHE). Logic J IGPL, special issue on Logic-based applications in AI. On-line first. https://doi.org/10.1093/jigpal/jzz029.

4. Falomir Z, Costa V, Gonzalez-Abril L. Obtaining discriminative colour names according to the context: using a fuzzy colour model and probabilistic reference grounding, international journal of uncertainty, fuzziness and knowledge-based systems. Special issue on Modelling Decisions in AI. 2019;27(Suppl. 1):107–42. https://doi.org/10.1142/S0218488519400063.

5. Ruiz FJ, Raya C, Samà A, Agell N. A transformational creativity tool to support chocolate designers. Pattern Recogn Lett. 2015;67(Part 1):75–80. https://doi.org/10.1016/j.patrec.2015.05.012.

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