What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Machine learning
His main research concerns Recommender system, Data mining, Personalization, Data science and Knowledge extraction.
As a part of the same scientific family, he mostly works in the field of Recommender system, focusing on Process and, on occasion, Focus.
His Data mining study combines topics in areas such as Customer lifetime value, Customer relationship management, Management science and Leverage.
His research investigates the link between Personalization and topics such as Profiling that cross with problems in User profile and One-to-one.
The various areas that he examines in his Knowledge extraction study include Structure, Machine learning and Management system.
To a larger extent, Alexander Tuzhilin studies Information retrieval with the aim of understanding Collaborative filtering.
His most cited work include:
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (7869 citations)
- Context-Aware Recommender Systems (1238 citations)
- Incorporating contextual information in recommender systems using a multidimensional approach (1034 citations)
What are the main themes of his work throughout his whole career to date?
Alexander Tuzhilin focuses on Recommender system, Data mining, Artificial intelligence, Information retrieval and Data science.
His Recommender system study incorporates themes from Process and Personalization.
His study in Data mining is interdisciplinary in nature, drawing from both Market segmentation and Temporal logic.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Natural language processing.
Alexander Tuzhilin works in the field of Information retrieval, namely Query language.
Alexander Tuzhilin studied Data science and Knowledge extraction that intersect with Structure.
He most often published in these fields:
- Recommender system (37.50%)
- Data mining (28.24%)
- Artificial intelligence (20.83%)
What were the highlights of his more recent work (between 2017-2021)?
- Recommender system (37.50%)
- Artificial intelligence (20.83%)
- Machine learning (12.96%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Machine learning, Information retrieval and Deep learning.
His Recommender system research is multidisciplinary, incorporating elements of Data science, Variety and Process.
Alexander Tuzhilin combines subjects such as Domain, Event, Pattern recognition and Natural language processing with his study of Artificial intelligence.
His Machine learning study integrates concerns from other disciplines, such as Sentence, Quality and Personalization.
His Deep learning research incorporates themes from Loan, Finance and Collaborative filtering.
His Collaborative filtering research is multidisciplinary, incorporating perspectives in Algorithm and Simple.
Between 2017 and 2021, his most popular works were:
- DDTCDR: Deep Dual Transfer Cross Domain Recommendation (13 citations)
- E.T.-RNN: Applying Deep Learning to Credit Loan Applications (13 citations)
- Recommending Remedial Learning Materials to Students by Filling Their Knowledge Gaps (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Alexander Tuzhilin spends much of his time researching Recommender system, Information retrieval, Artificial intelligence, Deep learning and Collaborative filtering.
Alexander Tuzhilin integrates Recommender system with Performance results in his research.
The Information retrieval study combines topics in areas such as Transfer of learning, Feature, Autoencoder and DUAL.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Quality, Sentence and Personalization.
His Deep learning study incorporates themes from Loan, Finance, Process and Domain.
His Collaborative filtering study frequently involves adjacent topics like Feature vector.
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