Publicaciones en las que colabora con ANTONIO BAHAMONDE RIONDA (43)

2023

  1. All-in-one picture: visual summary of items in a recommender system

    Neural Computing and Applications, Vol. 35, Núm. 27, pp. 20339-20349

  2. Users' photos of items can reveal their tastes in a recommender system

    Information Sciences, Vol. 642, pp. 119227

2019

  1. Optimizing novelty and diversity in recommendations

    Progress in Artificial Intelligence, Vol. 8, Núm. 1, pp. 101-109

2018

  1. A new method to learn growth curves of beef cattle using a factorization approach

    Computers and Electronics in Agriculture, Vol. 151, pp. 77-83

  2. A peer assessment method to provide feedback, consistent grading and reduce students’ burden in massive teaching settings

    Computers and Education, Vol. 126, pp. 283-295

  3. Representaciones basadas en redes neuronales para tareas de recomendación

    XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España

2016

  1. El deprendizax automáticu computacional na valoración d'exemplares bovinos d'Asturiana de los Valles

    Ciencies. Cartafueyos Asturianos de Ciencia y Teunoloxía: Revista de la Academia de la Llingua Asturiana, Núm. 6, pp. 46-61

  2. Using tensor products to detect unconditional label dependence in multilabel classifications

    Information Sciences, Vol. 329, pp. 20-32

2015

  1. A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments

    Knowledge-Based Systems, Vol. 85, pp. 322-328

  2. Analysis of nutrition data by means of a matrix factorization method

    Progress in Artificial Intelligence, Vol. 3, Núm. 3-4, pp. 119-127

  3. Including Content-Based Methods in Peer-Assessment of Open-Response Questions

    Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

  4. Mapping preferences into Euclidean space

    Expert Systems with Applications, Vol. 42, Núm. 22, pp. 8588-8596

  5. Optimizing different loss functions in multilabel classifications

    Progress in Artificial Intelligence, Vol. 3, Núm. 2, pp. 107-118

2012

  1. Binary relevance efficacy for multilabel classification

    Progress in Artificial Intelligence, Vol. 1, Núm. 4, pp. 303-313