What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- World Wide Web
- Programming language
His primary scientific interests are in Information retrieval, Recommender system, Ontology, Ontology and Artificial intelligence.
Pablo Castells does research in Information retrieval, focusing on Semantic Web specifically.
In his study, Pablo Castells carries out multidisciplinary Recommender system and Novelty research.
His Ontology research incorporates elements of Recommendation model, Semantics, Knowledge engineering and Domain.
As a part of the same scientific family, Pablo Castells mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Probabilistic logic.
His Semantic search research integrates issues from Concept search and Semantic Web Stack.
His most cited work include:
- Rank and relevance in novelty and diversity metrics for recommender systems (425 citations)
- An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval (364 citations)
- An ontology-based information retrieval model (214 citations)
What are the main themes of his work throughout his whole career to date?
Pablo Castells mostly deals with Information retrieval, Recommender system, World Wide Web, Artificial intelligence and Ontology.
He has researched Information retrieval in several fields, including Semantics, Context and Personalization.
His work on Collaborative filtering is typically connected to Novelty as part of general Recommender system study, connecting several disciplines of science.
His study looks at the relationship between World Wide Web and fields such as Multimedia, as well as how they intersect with chemical problems.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Performance prediction and Natural language processing.
His work on Upper ontology as part of general Ontology research is frequently linked to Ontology, thereby connecting diverse disciplines of science.
He most often published in these fields:
- Information retrieval (44.44%)
- Recommender system (41.98%)
- World Wide Web (29.01%)
What were the highlights of his more recent work (between 2016-2021)?
- Recommender system (41.98%)
- Artificial intelligence (22.22%)
- Information retrieval (44.44%)
In recent papers he was focusing on the following fields of study:
Pablo Castells focuses on Recommender system, Artificial intelligence, Information retrieval, Machine learning and Adaptation.
His work in the fields of Recommender system, such as Collaborative filtering, overlaps with other areas such as Popularity and Novelty.
His work on Question answering as part of general Artificial intelligence study is frequently linked to Coronavirus disease 2019, therefore connecting diverse disciplines of science.
His Information retrieval research includes elements of Context, Natural language processing, Task, Predictive modelling and Pattern recognition.
His study on Selection is often connected to Metric as part of broader study in Machine learning.
His studies deal with areas such as Feedback loop, Bearing and Task as well as Adaptation.
Between 2016 and 2021, his most popular works were:
- Statistical biases in Information Retrieval metrics for recommender systems (47 citations)
- Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems (41 citations)
- On the robustness and discriminative power of information retrieval metrics for top-N recommendation (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- World Wide Web
- Programming language
Pablo Castells spends much of his time researching Recommender system, Popularity, Artificial intelligence, Machine learning and Data science.
Information retrieval covers Pablo Castells research in Recommender system.
The various areas that he examines in his Information retrieval study include Matching, Interpretation, Empirical measure and Adaptation.
His research in Key focuses on subjects like Collaborative filtering, which are connected to Data mining.
His work deals with themes such as Test data, Statistical hypothesis testing, Test and Selection, which intersect with Pattern recognition.
His study in Robustness is interdisciplinary in nature, drawing from both Discounted cumulative gain and Discriminative model.
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.
