What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Alexandros Nanopoulos focuses on Recommender system, Data mining, Artificial intelligence, Information retrieval and Dimensionality reduction.
His work on Collaborative filtering as part of general Recommender system research is frequently linked to Credibility and Current generation, bridging the gap between disciplines.
His Association rule learning study in the realm of Data mining interacts with subjects such as SPQR tree.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
The Information retrieval study combines topics in areas such as Higher-order singular value decomposition, Similitude and Categorization.
His Dimensionality reduction research integrates issues from Latent semantic analysis, Metadata and Curse of dimensionality.
His most cited work include:
- Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data (388 citations)
- Learning optimal ranking with tensor factorization for tag recommendation (327 citations)
- Tag recommendations based on tensor dimensionality reduction (271 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Artificial intelligence, Recommender system, Machine learning and Information retrieval.
He combines subjects such as Cluster analysis, Similitude, Curse of dimensionality and Data set with his study of Data mining.
Alexandros Nanopoulos has included themes like Context and Pattern recognition in his Artificial intelligence study.
In the subject of general Recommender system, his work in Collaborative filtering is often linked to Matrix decomposition, thereby combining diverse domains of study.
His study in Machine learning is interdisciplinary in nature, drawing from both Quality, Dynamic time warping and Active learning.
The Information retrieval study combines topics in areas such as World Wide Web, Categorization and Dimensionality reduction.
He most often published in these fields:
- Data mining (34.71%)
- Artificial intelligence (33.53%)
- Recommender system (25.29%)
What were the highlights of his more recent work (between 2012-2018)?
- Data mining (34.71%)
- Artificial intelligence (33.53%)
- Recommender system (25.29%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data mining, Artificial intelligence, Recommender system, Machine learning and Social network.
His work is dedicated to discovering how Data mining, Data set are connected with E-commerce and Pattern recognition and other disciplines.
His Local outlier factor and Artificial neural network study in the realm of Artificial intelligence connects with subjects such as Regression and Point.
The concepts of his Recommender system study are interwoven with issues in Node, Active learning, Key and Personalization.
His work on Support vector machine and k-nearest neighbors algorithm as part of general Machine learning research is frequently linked to Gaussian, Scale and Noise, thereby connecting diverse disciplines of science.
His Social network study also includes
- Context, Crowds, Analytics and Internet privacy most often made with reference to Social media,
- Centrality, which have a strong connection to Marketing, Set, World Wide Web and Online advertising.
Between 2012 and 2018, his most popular works were:
- Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection (116 citations)
- Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions (78 citations)
- The Role of Emotions for the Perceived Usefulness in Online Customer Reviews (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Alexandros Nanopoulos mostly deals with Artificial intelligence, Data mining, Viral marketing, Social network and Recommender system.
His Artificial intelligence research is multidisciplinary, relying on both Context, Machine learning and E-commerce.
His Context study combines topics from a wide range of disciplines, such as Outlier and k-nearest neighbors algorithm.
His Data mining research includes elements of Euclidean distance and Pattern recognition.
In his study, he carries out multidisciplinary Recommender system and Current generation research.
His biological study spans a wide range of topics, including Anomaly detection and Clustering high-dimensional data.
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