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.
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.
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.
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.
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.
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
G. Adomavicius;A. Tuzhilin.
IEEE Transactions on Knowledge and Data Engineering (2005)
Context-Aware Recommender Systems
Gediminas Adomavicius;Bamshad Mobasher;Francesco Ricci;Alexander Tuzhilin.
Ai Magazine (2011)
Incorporating contextual information in recommender systems using a multidimensional approach
Gediminas Adomavicius;Ramesh Sankaranarayanan;Shahana Sen;Alexander Tuzhilin.
ACM Transactions on Information Systems (2005)
Selecting content for a user
Alexander S. Tuzhilin;Gediminas Adomavicius.
What makes patterns interesting in knowledge discovery systems
A. Silberschatz;A. Tuzhilin.
IEEE Transactions on Knowledge and Data Engineering (1996)
On subjective measures of interestingness in knowledge discovery
Avi Silberschatz;Alexander Tuzhilin.
knowledge discovery and data mining (1995)
Personalization technologies: a process-oriented perspective
Gediminas Adomavicius;Alexander Tuzhilin.
Communications of The ACM (2005)
The long tail of recommender systems and how to leverage it
Yoon-Joo Park;Alexander Tuzhilin.
conference on recommender systems (2008)
Using data mining methods to build customer profiles
G. Adomavicius;A. Tuzhilin.
IEEE Computer (2001)
An energy-efficient mobile recommender system
Yong Ge;Hui Xiong;Alexander Tuzhilin;Keli Xiao.
knowledge discovery and data mining (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: