A. Tuzhilin

发表

Recommender systems — beyond matrix completion

P. Resnick, D. Jannach, A. Tuzhilin, 2016, Commun. ACM.

E.T.-RNN: Applying Deep Learning to Credit Loan Applications

Alexander Tuzhilin, Dmitrii Babaev, Maxim Savchenko, 2019, KDD.

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

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2005, IEEE Transactions on Knowledge and Data Engineering.

On completeness of historical relational query languages

Albert Croker, James Clifford, Alexander Tuzhilin, 1991, TODS.

What Makes Patterns Interesting in Knowledge Discovery Systems

Abraham Silberschatz, Alexander Tuzhilin, A. Silberschatz, 1996, IEEE Trans. Knowl. Data Eng..

Unexpectedness as a Measure of Interestingness in Knowledge Discovery

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 1999, Decis. Support Syst..

On Subjective Measures of Interestingness in Knowledge Discovery

Abraham Silberschatz, Alexander Tuzhilin, A. Silberschatz, 1995, KDD.

Expert-Driven Validation of Set-Based Data Mining Results

E. Davis, A. Tuzhilin, Gediminas Adomavicius, 2002 .

In CARS We Trust: How Context-Aware Recommendations Affect Customers’ Trust and Other Business Performance Measures of Recommender Systems

A. Tuzhilin, Umberto Panniello, M. Gorgoglione, 2015 .

Research Note - In CARSs We Trust: How Context-Aware Recommendations Affect Customers' Trust and Other Business Performance Measures of Recommender Systems

Alexander Tuzhilin, Michele Gorgoglione, Umberto Panniello, 2016, Inf. Syst. Res..

Knowledge of the RS about the Contextual Factors Partially Observable UnobservableFully Observable Everything Known about Context Partial and Static Context Knowledge Latent Knowledge of Context Context Relevance Is Dynamic Partial and Dynamic Context Knowledge Nothing Is Known about Context Static

F. Ricci, A. Tuzhilin, B. Mobasher, 2011 .

Extending Recommender Systems : A Multidimensional Approach

A. Tuzhilin, Gediminas Adomavicius, 2001 .

The E-Butler Service, or has the Age of Electronic Personal Decision Making Assistants Arrived?

Alexander Tuzhilin, A. Tuzhilin, 1998 .

Knowledge evaluation: Other evaluations: minimum description length

A. Tuzhilin, 2002 .

Towards Analogy-based Recommendation Benchmarking of Perceived Analogy Semantics

A. Said, A. Tuzhilin, B. Mobasher, 2017 .

Letter to the editor [6]

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2006 .

Validation sequence optimization

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2007 .

Personalization technologies: A process-oriented perspective

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2006, Wirtschaftsinf..

Context-Aware Recommender Systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2015, Recommender Systems Handbook.

Context-aware recommender systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2008, ACM Conference on Recommender Systems.

Building and Querying Large Collections of Data Mining Models

A. Tuzhilin, Jie Hu, 2005 .

On the Completeness of Query Languages for Grouped and Ungrouped Historical Data Models

Albert Croker, James Clifford, Alexander Tuzhilin, 1993, Temporal Databases.

Estimating customer reviews in recommender systems using sentiment analysis methods

Bing Liu, A. Tuzhilin, Konstantin Bauman, 2015 .

Novel Perspectives in Collaborative Filtering Recommender Systems

A. Tuzhilin, 2014 .

Learning Latent Multi-Criteria Ratings From User Reviews for Recommendations

A. Tuzhilin, Pan Li, Alexander Tuzhilin, 2020, IEEE Transactions on Knowledge and Data Engineering.

It Driven Automation: The New Wave

A. Tuzhilin, 2004 .

From the Guest Co-Editors

Louiqa Raschid, Alexander Tuzhilin, L. Raschid, 2003, INFORMS J. Comput..

Adversarial Learning for Cross Domain Recommendations

A. Tuzhilin, Pan Li, B. Brost, 2022, ACM Transactions on Intelligent Systems and Technology.

SimTL: a simulation language based on temporal logic

Alexander Tuzhilin, A. Tuzhilin, 1992 .

CoLES: Contrastive Learning for Event Sequences with Self-Supervision

A. Tuzhilin, Gleb Gusev, Dmitrii Babaev, 2020, SIGMOD Conference.

A semantic approach to correctness of concurrent transaction executions

Paul G. Spirakis, Alexander Tuzhilin, A. Tuzhilin, 2015, PODS '85.

The Long Tail of Context: Does it Exist and Matter?

A. Tuzhilin, Konstantin Bauman, Alexey Vasilev, 2022, ArXiv.

Customer relationship management and Web mining: the next frontier

A. Tuzhilin, Alexander Tuzhilin, 2012, Data Mining and Knowledge Discovery.

Applications of temporal Databases to Knowledge-based Simulations

Alexander Tuzhilin, A. Tuzhilin, 1993, Temporal Databases.

Don’t Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain Embeddings

A. Tuzhilin, Pan Li, Moshe Unger, 2023, ACM Transactions on Management Information Systems.

Abstract-Driven Pattern Discovery in Databases

Vasant Dhar, Alexander Tuzhilin, V. Dhar, 1992, IEEE Trans. Knowl. Data Eng..

Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations pdf

Djoerd Hiemstra, Jaap Kamps, Chad Davis, 2016, ArXiv.

On Periodicity in Temporal Databases

James Clifford, Alexander Tuzhilin, A. Tuzhilin, 1995, Inf. Syst..

Knowledge Discovery from Databases: the Nyu Project

Vasant Dhar, James Clifford, Alexander Tuzhilin, 1995 .

Templar: a knowledge-based language for software specifications using temporal logic

Alexander Tuzhilin, A. Tuzhilin, 1991, TOIS.

On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 2003, Manag. Sci..

A Temporal Relational Algebra as a Basis for Temporal Relational Completeness

James Clifford, Alexander Tuzhilin, A. Tuzhilin, 1990, VLDB.

Divide and prosper: comparing models of customer behavior from populations to individuals

Tianyi Jiang, Alexander Tuzhilin, A. Tuzhilin, 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

CoLES: Contrastive Learning for Event Sequences with Self-Supervision

A. Tuzhilin, Gleb Gusev, Dmitrii Babaev, 2020, SIGMOD Conference.

Event sequence metric learning pdf

Gleb Gusev, Mariya Ivanova, Alexander Tuzhilin, 2020, ArXiv.

Towards the Next Generation of Recommender Systems

Alexander Tuzhilin, A. Tuzhilin, Alexander Tuzhilin, 2010, ICE-B 2010.

YASENN: Explaining Neural Networks via Partitioning Activation Sequences pdf

Yaroslav Zharov, Alexander Tuzhilin, Pavel Shvechikov, 2018, ArXiv.

Handling very large numbers of association rules in the analysis of microarray data

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2002, KDD.

Personalization and Recommender Systems

Gediminas Adomavicius, Zan Huang, Alexander Tuzhilin, 2008 .

Context-aware recommender systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2008, RecSys '08.

Incorporating contextual information in recommender systems using a multidimensional approach

Gediminas Adomavicius, Alexander Tuzhilin, Ramesh Sankaranarayanan, 2005, TOIS.

Discovering Unexpected Patterns in Temporal Data Using Temporal Logic

Alexander Tuzhilin, Gideon Berger, A. Tuzhilin, 1997, Temporal Databases, Dagstuhl.

Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models

Akhmed Umyarov, Alexander Tuzhilin, A. Tuzhilin, 2009, RecSys '09.

Don’t Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain Embeddings

A. Tuzhilin, Pan Li, Moshe Unger, 2023, ACM Trans. Manag. Inf. Syst..

User-Assisted Knowledge Discovery: How Much Should the User Be Involved

Alexander Tuzhilin, Avi Silberschatz, A. Silberschatz, 1996 .

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

Bing Liu, Alexander Tuzhilin, Konstantin Bauman, 2017, KDD.

Social and Economic Computing Introduction

Wenji Mao, Jonathan Gratch, Alexander Tuzhilin, 2011 .

Using Query-Driven Simulations for Querying Outcomes of Business Processes

Alexander Tuzhilin, P. Balasubramanian, A. Tuzhilin, 1996, Decis. Support Syst..

An Architecture of e-Butler: A Consumer-Centric Online Personalization System

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2002, Int. J. Comput. Intell. Appl..

Knowledge refinement based on the discovery of unexpected patterns in data mining

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 2002, Decis. Support Syst..

Extending temporal logic to support high-level simulations

Alexander Tuzhilin, A. Tuzhilin, 1995, TOMC.

On the Expressive Power of Infinite Temporal Databases

Gabriel M. Kuper, Alexander Tuzhilin, A. Tuzhilin, 1992 .

Modeling Dynamics of Databases with Relational Discrete Event Systems and Models

Alexander Tuzhilin, Zvi M. Kedem, Z. Kedem, 1991 .

Relational database behavior: utilizing relational discrete event systems and models

Alexander Tuzhilin, Zvi M. Kedem, Z. Kedem, 1989, PODS.

Improving Personalization Solutions through Optimal Segmentation of Customer Bases

Tianyi Jiang, Alexander Tuzhilin, A. Tuzhilin, 2006, IEEE Transactions on Knowledge and Data Engineering.

Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?

Tianyi Jiang, Alexander Tuzhilin, A. Tuzhilin, 2006, IEEE Transactions on Knowledge and Data Engineering.

Knowledge evaluation: Other evaluations: usefulness, novelty, and integration of interesting news measures

Alexander Tuzhilin, A. Tuzhilin, 2002 .

Finding unexpected patterns in data

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 2002 .

Temporal Logic as a Simulation Language

Alexander Tuzhilin, A. Tuzhilin, 1990 .

Mining patterns that respond to actions

Ke Wang, Ada Wai-Chee Fu, Alexander Tuzhilin, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Discovery of Actionable Patterns in Databases: the Action Hierarchy Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 1997, KDD.

Mining Actionable Patterns by Role Models

Ke Wang, Alexander Tuzhilin, Yuelong Jiang, 2006, 22nd International Conference on Data Engineering (ICDE'06).

Using Context to Improve Predictive Modeling of Customers in Personalization Applications

Alexander Tuzhilin, Michele Gorgoglione, Cosimo Palmisano, 2008, IEEE Transactions on Knowledge and Data Engineering.

Towards Controllable and Personalized Review Generation pdf

Pan Li, Alexander Tuzhilin, A. Tuzhilin, 2019, EMNLP.

REQUEST: A Query Language for Customizing Recommendations

Rong Zheng, Gediminas Adomavicius, Alexander Tuzhilin, 2011, Inf. Syst. Res..

Dynamic micro-targeting: fitness-based approach to predicting individual preferences

Tianyi Jiang, Alexander Tuzhilin, A. Tuzhilin, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Personalization technologies

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2005, Commun. ACM.

Making Sense of Marketing Data: Some MIS Perspectives on the Analysis of Large Data Sets

Alexander Tuzhilin, Shahana Sen, A. Tuzhilin, 1998 .

Recommendation Technologies: Survey of Current Methods and Possible Extensions

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2003 .

Validation Sequence Optimization: A Theoretical Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2007, INFORMS J. Comput..

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

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2004, Data Mining and Knowledge Discovery.

On Characterization and Discovery of Minimal Unexpected Patterns in Data Mining Applications

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 2003 .

A Belief-Driven Method for Discovering Unexpected Patterns

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 1998, KDD.

Editor's introduction to the special issue on knowledge discovery and its applications to business decision-making

Alexander Tuzhilin, A. Tuzhilin, 1997, Decis. Support Syst..

A Belief-Driven Discovery Framework Based on Data Monitoring and Triggering

Alexander Tuzhilin, Avi Silberschatz, A. Silberschatz, 1996 .

Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction

Zhichao Jiang, Alexander Tuzhilin, Pan Li, 2021, KDD.

Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations pdf

Alexander Tuzhilin, Pan Li, A. Tuzhilin, 2021, IEEE Transactions on Knowledge and Data Engineering.

Comparing context-aware recommender systems in terms of accuracy and diversity

Alexander Tuzhilin, Michele Gorgoglione, Umberto Panniello, 2012, User Modeling and User-Adapted Interaction.

Context-Aware Recommendations Based on Deep Learning Frameworks

Moshe Unger, Alexander Tuzhilin, Amit Livne, 2020, ACM Trans. Manag. Inf. Syst..

The long tail of recommender systems and how to leverage it

Alexander Tuzhilin, Yoon-Joo Park, A. Tuzhilin, 2008, RecSys '08.

Where to Go on Your Next Trip?: Optimizing Travel Destinations Based on User Preferences

Djoerd Hiemstra, Jaap Kamps, Chad Davis, 2015, SIGIR.

Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks

Alexander Tuzhilin, Valentin Khrulkov, Ivan V. Oseledets, 2020, RecSys.

Cost-Aware Collaborative Filtering for Travel Tour Recommendations

Hui Xiong, Qi Liu, Yong Ge, 2014, TOIS.

Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

Alexander Tuzhilin, Konstantin Bauman, A. Tuzhilin, 2021, Inf. Syst. Res..

Route Recommendations for Intelligent Transportation Services

Huayu Li, Yong Ge, Alexander Tuzhilin, 2021, IEEE Transactions on Knowledge and Data Engineering.

Workshop on Context-Aware Recommender Systems

F. Ricci, A. Tuzhilin, B. Mobasher, 2020, RecSys.

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Alexander Tuzhilin, Zhichao Jiang, Pan Li, 2020, RecSys.

Workshop on context-aware recommender systems

Gediminas Adomavicius, Bamshad Mobasher, Moshe Unger, 2019, RecSys.

Discovering Contextual Information from User Reviews for Recommendation Purposes

Alexander Tuzhilin, Konstantin Bauman, A. Tuzhilin, 2014, CBRecSys@RecSys.

The effect of context-aware recommendations on customer purchasing behavior and trust

Alexander Tuzhilin, Michele Gorgoglione, Umberto Panniello, 2011, RecSys '11.

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

Alexander Tuzhilin, Michele Gorgoglione, Cosimo Palmisano, 2009, RecSys '09.

Multidimensional Recommender Systems: A Data Warehousing Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2001, WELCOM.

Workshop on Recommendation in Complex Scenarios

Marijn Koolen, Toine Bogers, Bamshad Mobasher, 2017, ACM Conference on Recommender Systems.

Recommending Remedial Learning Materials to Students by Filling Their Knowledge Gaps

Alexander Tuzhilin, Konstantin Bauman, A. Tuzhilin, 2018, MIS Q..

Querying multiple sets of discovered rules

Bing Liu, Alexander Tuzhilin, B. Liu, 2002, KDD.

User profiling in personalization applications through rule discovery and validation

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 1999, KDD '99.

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

Hui Xiong, Shashi Shekhar, Alexander Tuzhilin, 2013, ACM Trans. Intell. Syst. Technol..

Small is beautiful: discovering the minimal set of unexpected patterns

Balaji Padmanabhan, Alexander Tuzhilin, A. Tuzhilin, 2000, KDD '00.

Integrating User Behavior and Collaborative Methods in Recommender Systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2004 .

On Unexpectedness in Recommender Systems

Panagiotis Adamopoulos, Alexander Tuzhilin, A. Tuzhilin, 2013, ACM Trans. Intell. Syst. Technol..

On the discovery of significant statistical quantitative rules

Balaji Padmanabhan, Hong Zhang, Alexander Tuzhilin, 2004, KDD.

Noise-Resilient Automatic Interpretation of Holter ECG Recordings pdf

Manvel Avetisian, Alexander Tuzhilin, Elena Sokolova, 2020, BIOSIGNALS.

Proceedings of the third ACM conference on Recommender systems

Lars Schmidt-Thieme, Robin Burke, Alexander Felfernig, 2008, RecSys 2008.

ComplexRec 2017: Recommendation in Complex Scenarios

Marijn Koolen, Alan Said, Bamshad Mobasher, 2017, ACM Conference on Recommender Systems.

DDTCDR: Deep Dual Transfer Cross Domain Recommendation

Pan Li, Alexander Tuzhilin, A. Tuzhilin, 2019, WSDM.

Cost-aware travel tour recommendation

Hui Xiong, Qi Liu, Jian Chen, 2011, KDD.

An energy-efficient mobile recommender system

Hui Xiong, Marco Gruteser, Michael J. Pazzani, 2010, KDD.

Adversarial Learning for Cross Domain Recommendations

A. Tuzhilin, Pan Li, B. Brost, 2022, ACM Trans. Intell. Syst. Technol..

Radiologist-Level Stroke Classification on Non-contrast CT Scans with Deep U-Net pdf

Manvel Avetisian, Alexander Tuzhilin, Vladimir Kokh, 2019, MICCAI.

Using Social Sensors for Detecting Emergency Events

Alexander Tuzhilin, Konstantin Bauman, Ryan Zaczynski, 2017, ACM Trans. Manag. Inf. Syst..

On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems

Alexander Tuzhilin, Panagiotis Adamopoulos, A. Tuzhilin, 2014, RecSys '14.

Report on the KDD2000 panel personalization and data mining: exploring the synergies

Alexander Tuzhilin, A. Tuzhilin, 2000, SKDD.

Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks

A. Tuzhilin, Pavel Blinov, Manvel Avetisian, 2020, AIME.

LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

A. Ryzhkov, A. Tuzhilin, M. Savchenko, 2021, ArXiv.

Recommendation strategies in personalization applications

Alexander Tuzhilin, Michele Gorgoglione, Umberto Panniello, 2019, Inf. Manag..

The Business Value of Recommendations: A Privacy-Preserving Econometric Analysis

Panagiotis Adamopoulos, Alexander Tuzhilin, A. Tuzhilin, 2015, ICIS.

Leveraging aggregate ratings for better recommendations

Akhmed Umyarov, Alexander Tuzhilin, A. Tuzhilin, 2007, RecSys '07.

Deep Multi-Objective Multi-Stakeholder Music Recommendation

Maxime C. Cohen, Moshe Unger, Alexander Tuzhilin, 2021 .

Using Data Mining Methods to Build Customer Profiles

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2001, Computer.

Context-Aware Recommender Systems: From Foundations to Recent Developments

A. Tuzhilin, Konstantin Bauman, G. Adomavicius, 2022, Recommender Systems Handbook.

Latent Unexpected Recommendations

Alexander Tuzhilin, Pan Li, A. Tuzhilin, 2020, ACM Trans. Intell. Syst. Technol..

Recommender System: Review

Jonathan L. Herlocker, M. Pazzani, Qing Li, 2013 .

Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments

Panagiotis Adamopoulos, Anindya Ghose, Alexander Tuzhilin, 2016, MIS Q..

The Effect of Context on the Predictive Performance of Segmentation

Alexander Tuzhilin, Maria Francesca Faraone, Michele Gorgoglione, 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

On Temporal Grouping

Albert Croker, Fabio Grandi, James Clifford, 1995, Temporal Databases.

Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2018 .

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems

Panagiotis Adamopoulos, Alexander Tuzhilin, A. Tuzhilin, 2013, RecSys.

Probabilistic Neighborhood Selection in Collaborative Filtering Systems

Panagiotis Adamopoulos, Alexander Tuzhilin, A. Tuzhilin, 2013 .

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

Panagiotis Adamopoulos, Alexander Tuzhilin, A. Tuzhilin, 2011, DiveRS@RecSys.

Using relational discrete event systems and models for prediction of future behavior of databases

Alexander Tuzhilin, Zvi M. Kedem, A. Tuzhilin, 1989 .

Improving Collaborative Filtering Recommendations Using External Data

Akhmed Umyarov, Alexander Tuzhilin, A. Tuzhilin, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Knowledge management revisited

Alexander Tuzhilin, A. Tuzhilin, 2011, ACM Trans. Manag. Inf. Syst..

Querying and controlling the future behavior of complex objects

Alexander Tuzhilin, Zvi M. Kedem, Z. Kedem, 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.