D-Index & Metrics Best Publications

Gediminas Adomavicius

University of Minnesota
United States

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 42 Citations 26,562 137 World Ranking 5112 National Ranking 2515

Overview

What is he best known for?

The fields of study he is best known for:

Statistics
Artificial intelligence
Machine learning

His scientific interests lie mostly in Recommender system, Data mining, Personalization, Collaborative filtering and Profiling. His work in the fields of Slope One and MovieLens overlaps with other areas such as Contextual information. His Data mining research includes elements of Ranking and Data science.

In general Collaborative filtering, his work in Cold start is often linked to Class, Context model and Content analysis linking many areas of study. The concepts of his Profiling study are interwoven with issues in User profile and One-to-one. His research in the fields of Recommendation quality overlaps with other disciplines such as Multi criteria.

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?

His primary scientific interests are in Recommender system, Data mining, World Wide Web, Artificial intelligence and Machine learning. His work in the fields of Recommender system, such as Collaborative filtering, intersects with other areas such as Contextual information. Gediminas Adomavicius combines Collaborative filtering and Diversity in his studies.

His Data mining research incorporates elements of Scalability and Cluster analysis. His work on Random forest as part of general Artificial intelligence study is frequently linked to Property, therefore connecting diverse disciplines of science. The various areas that he examines in his Preference study include Behavioral economics, Human–computer interaction and Willingness to pay.

He most often published in these fields:

Recommender system (43.98%)
Data mining (22.29%)
World Wide Web (15.66%)

What were the highlights of his more recent work (between 2018-2021)?

Recommender system (43.98%)
Artificial intelligence (12.65%)
Machine learning (12.65%)

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

Gediminas Adomavicius focuses on Recommender system, Artificial intelligence, Machine learning, Consumption and Data science. His Recommender system study is concerned with the field of Information retrieval as a whole. Gediminas Adomavicius interconnects Covariate and Instrumental variable in the investigation of issues within Artificial intelligence.

His Machine learning research includes themes of Tree, Reliability and Causal inference. The various areas that he examines in his Data science study include Resolution and Dimension. His Personalization study deals with Information access intersecting with Knowledge management, The Internet and User experience design.

Between 2018 and 2021, his most popular works were:

Beyond Personalization: Research Directions in Multistakeholder Recommendation. (37 citations)
Multistakeholder recommendation: Survey and research directions (34 citations)
Understanding User-Generated Content and Customer Engagement on Facebook Business Pages (13 citations)

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.

Best Publications

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)

13295 Citations

Context-Aware Recommender Systems

Gediminas Adomavicius;Bamshad Mobasher;Francesco Ricci;Alexander Tuzhilin.
Ai Magazine (2011)

2764 Citations

Incorporating contextual information in recommender systems using a multidimensional approach

Gediminas Adomavicius;Ramesh Sankaranarayanan;Shahana Sen;Alexander Tuzhilin.
ACM Transactions on Information Systems (2005)

1671 Citations

Selecting content for a user

Alexander S. Tuzhilin;Gediminas Adomavicius.
(2015)

1235 Citations

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

G. Adomavicius;YoungOk Kwon.
IEEE Transactions on Knowledge and Data Engineering (2012)

727 Citations

New Recommendation Techniques for Multicriteria Rating Systems

G. Adomavicius;YoungOk Kwon.
IEEE Intelligent Systems (2007)

609 Citations

Personalization technologies: a process-oriented perspective

Gediminas Adomavicius;Alexander Tuzhilin.
Communications of The ACM (2005)

509 Citations

Using data mining methods to build customer profiles

G. Adomavicius;A. Tuzhilin.
IEEE Computer (2001)

420 Citations

Multi-Criteria Recommender Systems

Gediminas Adomavicius;YoungOk Kwon.
Springer US (2015)

370 Citations

Context-aware recommender systems

Gediminas Adomavicius;Alexander Tuzhilin.
conference on recommender systems (2008)

311 Citations

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