H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 8,547 70 World Ranking 8180 National Ranking 3804

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Alexandros Karatzoglou spends much of his time researching Artificial intelligence, Recommender system, Machine learning, Collaborative filtering and Recurrent neural network. His Artificial intelligence research integrates issues from Pattern recognition and Implementation. He integrates several fields in his works, including Recommender system and Matrix decomposition.

His study in the field of Ranking, Relevance vector machine, Structured support vector machine and Support vector machine is also linked to topics like Regression. His Collaborative filtering research focuses on subjects like Data mining, which are linked to Mean reciprocal rank and Relevance. His Recurrent neural network study deals with Session intersecting with Personalization, Video streaming and Multimedia.

His most cited work include:

  • kernlab - An S4 Package for Kernel Methods in R (1142 citations)
  • Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (589 citations)
  • Session-based Recommendations with Recurrent Neural Networks (423 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Recommender system, Artificial intelligence, Machine learning, Collaborative filtering and Data mining. His biological study spans a wide range of topics, including Learning to rank and Session. His Artificial neural network and Deep learning study in the realm of Artificial intelligence connects with subjects such as Task, Context and Space.

In his study, Adaptation and Convolutional neural network is inextricably linked to Benchmark, which falls within the broad field of Machine learning. He has included themes like Algorithm and Theoretical computer science in his Collaborative filtering study. His work carried out in the field of Algorithm brings together such families of science as Radial basis function kernel, Kernel embedding of distributions, Tree kernel, Fuzzy clustering and Kernel principal component analysis.

He most often published in these fields:

  • Recommender system (53.26%)
  • Artificial intelligence (51.09%)
  • Machine learning (42.39%)

What were the highlights of his more recent work (between 2017-2021)?

  • Recommender system (53.26%)
  • Artificial intelligence (51.09%)
  • Machine learning (42.39%)

In recent papers he was focusing on the following fields of study:

Alexandros Karatzoglou focuses on Recommender system, Artificial intelligence, Machine learning, Artificial neural network and Task. In general Recommender system, his work in Collaborative filtering is often linked to Context linking many areas of study. His Collaborative filtering study combines topics in areas such as Recurrent neural network, Session and Usage data.

Artificial intelligence is frequently linked to Layer in his study. His work focuses on many connections between Machine learning and other disciplines, such as Sequence learning, that overlap with his field of interest in Hyperparameter, Control, Stability and Stochastic gradient descent. His research investigates the link between Artificial neural network and topics such as Representation that cross with problems in Adaptation and Benchmark.

Between 2017 and 2021, his most popular works were:

  • Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (228 citations)
  • Overcoming catastrophic forgetting with hard attention to the task (115 citations)
  • Overcoming catastrophic forgetting with hard attention to the task (109 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Alexandros Karatzoglou mainly investigates Recommender system, Machine learning, Artificial intelligence, Forgetting and Task. His study in the fields of Collaborative filtering under the domain of Recommender system overlaps with other disciplines such as Order. Much of his study explores Machine learning relationship to Sequence learning.

He has researched Sequence learning in several fields, including Pooling, Residual, Generative grammar, Generative model and Convolutional neural network. His research in Forgetting intersects with topics in Stability, Artificial neural network, Stochastic gradient descent and Hyperparameter. Alexandros Karatzoglou applies his multidisciplinary studies on Task and Control in his research.

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.

Top Publications

kernlab - An S4 Package for Kernel Methods in R

Alexandros Karatzoglou;Alexandros Smola;Kurt Hornik;Achim Zeileis.
Journal of Statistical Software (2004)

1651 Citations

Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering

Alexandros Karatzoglou;Xavier Amatriain;Linas Baltrunas;Nuria Oliver.
conference on recommender systems (2010)

869 Citations

Session-based Recommendations with Recurrent Neural Networks

Balázs Hidasi;Alexandros Karatzoglou;Linas Baltrunas;Domonkos Tikk.
international conference on learning representations (2016)

760 Citations

Support Vector Machines in R

Alexandros Karatzoglou;David Meyer;Kurt Hornik.
Journal of Statistical Software (2006)

634 Citations

CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering

Yue Shi;Alexandros Karatzoglou;Linas Baltrunas;Martha Larson.
conference on recommender systems (2012)

371 Citations

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

Balázs Hidasi;Massimo Quadrana;Alexandros Karatzoglou;Domonkos Tikk.
conference on recommender systems (2016)

322 Citations

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

Balázs Hidasi;Alexandros Karatzoglou.
conference on information and knowledge management (2018)

321 Citations

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Massimo Quadrana;Alexandros Karatzoglou;Balázs Hidasi;Paolo Cremonesi.
conference on recommender systems (2017)

301 Citations

TFMAP: optimizing MAP for top-n context-aware recommendation

Yue Shi;Alexandros Karatzoglou;Linas Baltrunas;Martha Larson.
international acm sigir conference on research and development in information retrieval (2012)

256 Citations

Collaborative Filtering Bandits

Shuai Li;Alexandros Karatzoglou;Claudio Gentile.
international acm sigir conference on research and development in information retrieval (2016)

224 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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