What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Algorithm
Antonio Hernando focuses on Collaborative filtering, Recommender system, Condensed matter physics, Amorphous solid and Data mining.
His study in Collaborative filtering is interdisciplinary in nature, drawing from both Process and Artificial intelligence.
His study in the field of Cold start also crosses realms of Quality.
His studies deal with areas such as Magnetic anisotropy, Magnetization, Annealing and Magnetostriction as well as Condensed matter physics.
The concepts of his Amorphous solid study are interwoven with issues in Micromagnetics, Composite material, Nuclear magnetic resonance and Nanocrystalline material.
His Nanocrystalline material study combines topics in areas such as Crystallization and Coercivity.
His most cited work include:
- Recommender systems survey (1797 citations)
- A collaborative filtering approach to mitigate the new user cold start problem (288 citations)
- Collaborative filtering adapted to recommender systems of e-learning (195 citations)
What are the main themes of his work throughout his whole career to date?
Antonio Hernando mainly investigates Condensed matter physics, Ferromagnetism, Amorphous solid, Magnetization and Coercivity.
His research integrates issues of Magnetic anisotropy, Amorphous metal, Nuclear magnetic resonance, Magnetostriction and Anisotropy in his study of Condensed matter physics.
His Nuclear magnetic resonance research includes themes of Crystallite, Spin glass, Hysteresis and Saturation.
His Ferromagnetism research includes elements of Crystallography, Paramagnetism, Magnetoresistance, Magnetic moment and Antiferromagnetism.
His Amorphous solid research is multidisciplinary, incorporating perspectives in Crystallization and Analytical chemistry.
His work carried out in the field of Coercivity brings together such families of science as Sputtering, Nanostructure, Magnetic hysteresis, Magnetic shape-memory alloy and Nanocrystalline material.
He most often published in these fields:
- Condensed matter physics (55.26%)
- Ferromagnetism (29.82%)
- Amorphous solid (18.86%)
What were the highlights of his more recent work (between 2011-2021)?
- Collaborative filtering (10.96%)
- Recommender system (10.96%)
- Condensed matter physics (55.26%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Collaborative filtering, Recommender system, Condensed matter physics, Ferromagnetism and Artificial intelligence.
His Collaborative filtering research incorporates elements of Similarity measure and Data mining.
His study in the fields of Cold start under the domain of Recommender system overlaps with other disciplines such as Reliability.
The Condensed matter physics study combines topics in areas such as Electromagnetic radiation and Nuclear magnetic resonance.
His Ferromagnetism research is multidisciplinary, incorporating elements of Scattering, Nanotechnology, Coercivity and Antiferromagnetism.
The various areas that he examines in his Artificial intelligence study include Machine learning and Pattern recognition.
Between 2011 and 2021, his most popular works were:
- Recommender systems survey (1797 citations)
- A collaborative filtering approach to mitigate the new user cold start problem (288 citations)
- A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model (158 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
His primary areas of investigation include Collaborative filtering, Recommender system, Information retrieval, MovieLens and Data mining.
His Collaborative filtering research is multidisciplinary, incorporating elements of Similarity measure, Artificial intelligence and Process.
His study in the field of Artificial neural network and Deep learning is also linked to topics like Abstraction and Reliability.
When carried out as part of a general Recommender system research project, his work on Cold start is frequently linked to work in Reliability, therefore connecting diverse disciplines of study.
Antonio Hernando works mostly in the field of MovieLens, limiting it down to topics relating to Similarity and, in certain cases, Product and Singularity, as a part of the same area of interest.
His work carried out in the field of Data mining brings together such families of science as Similarity and Metric.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
