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Alexander Tuzhilin

Alexander Tuzhilin

D-Index & Metrics

Computer Science

D-Index
58
Citations
32334
World Ranking
3521
National Ranking
1694

Overview

Alexander Tuzhilin is affiliated with New York University in the United States. Their research primarily spans the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Information Systems, Management Science and Operations Research, Marketing, and Mechanical Engineering.

Their work covers topics including Recommender Systems and Techniques, Advanced Bandit Algorithms Research, Consumer Market Behavior and Pricing, Sentiment Analysis and Opinion Mining, Data Stream Mining Techniques, Human Mobility and Location-Based Analysis, and Topic Modeling.

Alexander Tuzhilin's frequent publication venues include arXiv (Cornell University), SSRN Electronic Journal, IEEE Transactions on Knowledge and Data Engineering, Information Systems Research, and Russian Metallurgy (Metally).

Recent papers authored include:

  • Context-Aware Recommendations Based on Deep Learning Frameworks (2020), ACM Transactions on Management Information Systems
  • Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations (2021), IEEE Transactions on Knowledge and Data Engineering
  • Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes (2021), Information Systems Research
  • Hierarchical Latent Context Representation for Context-Aware Recommendations (2020), IEEE Transactions on Knowledge and Data Engineering
  • CoLES: Contrastive Learning for Event Sequences with Self-Supervision (2022), Proceedings of the 2022 International Conference on Management of Data

Frequent co-authors of their research are:

  • Moshe Unger
  • Pan Li
  • Konstantin Bauman
  • Brian Brost

Best Publications

  • Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

    G. Adomavicius;A. Tuzhilin

  • Context-Aware Recommender Systems

    Gediminas Adomavicius;Bamshad Mobasher;Francesco Ricci;Alexander Tuzhilin

  • Incorporating contextual information in recommender systems using a multidimensional approach

    Gediminas Adomavicius;Ramesh Sankaranarayanan;Shahana Sen;Alexander Tuzhilin

  • Selecting content for a user

    Alexander S. Tuzhilin;Gediminas Adomavicius

  • What makes patterns interesting in knowledge discovery systems

    A. Silberschatz;A. Tuzhilin

  • On subjective measures of interestingness in knowledge discovery

    Avi Silberschatz;Alexander Tuzhilin

  • The long tail of recommender systems and how to leverage it

    Yoon-Joo Park;Alexander Tuzhilin

  • Personalization technologies: a process-oriented perspective

    Gediminas Adomavicius;Alexander Tuzhilin

  • Using data mining methods to build customer profiles

    G. Adomavicius;A. Tuzhilin

  • An energy-efficient mobile recommender system

    Yong Ge;Hui Xiong;Alexander Tuzhilin;Keli Xiao

  • On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

    Panagiotis Adamopoulos;Alexander Tuzhilin

  • Context-aware recommender systems

    Gediminas Adomavicius;Alexander Tuzhilin

  • Introduction to special section on intelligent mobile knowledge discovery and management systems

    Hui Xiong;Shashi Shekhar;Alexander Tuzhilin

  • Unexpectedness as a measure of interestingness in knowledge discovery

    Balaji Padmanabhan;Alexander Tuzhilin

  • Using Context to Improve Predictive Modeling of Customers in Personalization Applications

    C. Palmisano;A. Tuzhilin;M. Gorgoglione

  • Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

    Umberto Panniello;Alexander Tuzhilin;Michele Gorgoglione;Cosimo Palmisano

  • Expert-Driven Validation of Rule-Based User Models in Personalization Applications

    Gediminas Adomavicius;Alexander Tuzhilin

  • DDTCDR: Deep Dual Transfer Cross Domain Recommendation

    Pan Li;Alexander Tuzhilin

  • User profiling in personalization applications through rule discovery and validation

    Gediminas Adomavicius;Alexander Tuzhilin

  • Recommender systems — beyond matrix completion

    Dietmar Jannach;Paul Resnick;Alexander Tuzhilin;Markus Zanker

  • Towards the Next Generation of Recommender Systems

    Alexander Tuzhilin

  • Personalization technologies: A process-oriented perspective

    D. Adomavicius;A. Tuzhilin

Frequent Co-Authors

Gediminas Adomavicius
Gediminas Adomavicius University of Minnesota
Bamshad Mobasher
Bamshad Mobasher DePaul University
Bing Liu
Bing Liu University of Illinois at Chicago
Hui Xiong
Hui Xiong Rutgers, The State University of New Jersey
Yong Ge
Yong Ge University of Arizona
Rong Zheng
Rong Zheng McMaster University
Francesco Ricci
Francesco Ricci Free University of Bozen-Bolzano
Djoerd Hiemstra
Djoerd Hiemstra Radboud University
Jaap Kamps
Jaap Kamps University of Amsterdam
Jonathan Gratch
Jonathan Gratch University of Southern California

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