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Displaying 1-37 out of 37 total
Social and Economic Computing
Found in: IEEE Intelligent Systems
By Wenji Mao,Alexander Tuzhilin,Jonathan Gratch
Issue Date:November 2011
pp. 19-21
Social and economic computing is a cross-disciplinary field focusing on the development of computing technologies that consider social and economic contexts. Social computing and economic computing not only share a number of computing technologies, they al...
 
Improving Collaborative Filtering Recommendations Using External Data
Found in: Data Mining, IEEE International Conference on
By Akhmed Umyarov, Alexander Tuzhilin
Issue Date:December 2008
pp. 618-627
This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of collaborative filtering (CF) methods. For a statistical model-based CF approach, we formally showed that this additional aggregated ...
 
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
Found in: IEEE Transactions on Knowledge and Data Engineering
By Tianyi Jiang, Alexander Tuzhilin
Issue Date:March 2009
pp. 305-320
On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more personalized products and services to them. Traditionally, customer segmentation is achie...
 
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
Found in: IEEE Transactions on Knowledge and Data Engineering
By Cosimo Palmisano, Alexander Tuzhilin, Michele Gorgoglione
Issue Date:November 2008
pp. 1535-1549
The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in pers...
 
Dynamic Micro Targeting: Fitness-Based Approach to Predicting Individual Preferences
Found in: Data Mining, IEEE International Conference on
By Tianyi Jiang, Alexander Tuzhilin
Issue Date:October 2007
pp. 173-182
It is crucial to segment customers intelligently in order to offer more targeted and personalized products and services. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data...
 
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
Found in: Data Mining, IEEE International Conference on
By Tianyi Jiang, Alexander Tuzhilin
Issue Date:December 2006
pp. 307-318
On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmenta...
 
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
Found in: IEEE Transactions on Knowledge and Data Engineering
By Tianyi Jiang, Alexander Tuzhilin
Issue Date:October 2006
pp. 1297-1311
There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist ...
 
Mining Actionable Patterns by Role Models
Found in: Data Engineering, International Conference on
By Ke Wang, Yuelong Jiang, Alexander Tuzhilin
Issue Date:April 2006
pp. 16
Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user.
 
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
Found in: IEEE Transactions on Knowledge and Data Engineering
By Balaji Padmanabhan, Alexander Tuzhilin
Issue Date:February 2006
pp. 202-216
A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we pre...
 
Mining Patterns That Respond to Actions
Found in: Data Mining, IEEE International Conference on
By Yuelong Jiang, Ke Wang, Alexander Tuzhilin, Ada Wai-Chee Fu
Issue Date:November 2005
pp. 669-672
Data mining focuses on patterns that summarize the data. In this paper, we focus on mining patterns that could change the state by responding to opportunities of actions.
 
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
Found in: IEEE Transactions on Knowledge and Data Engineering
By Gediminas Adomavicius, Alexander Tuzhilin
Issue Date:June 2005
pp. 734-749
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation...
 
Divide and Prosper: Comparing Models of Customer Behavior From Populations to Individuals
Found in: Data Mining, IEEE International Conference on
By Tianyi Jiang, Alexander Tuzhilin
Issue Date:November 2004
pp. 419-422
This paper compares customer segmentation, 1-to-1, and aggregate marketing approaches across a broad range of experimental settings, including multiple segmentation levels, marketing datasets, dependent variables, and different types of classifiers, segmen...
 
Using Data Mining Methods to Build Customer Profiles
Found in: Computer
By Gediminas Adomavicius, Alexander Tuzhilin
Issue Date:February 2001
pp. 74-82
<p>Personalization--the ability to provide content and services tailored to individuals' preferences and behavior--is an important marketing tool. The authors developed an approach that uses information from customers' transactional histories to cons...
 
What Makes Patterns Interesting in Knowledge Discovery Systems
Found in: IEEE Transactions on Knowledge and Data Engineering
By Avi Silberschatz, Alexander Tuzhilin
Issue Date:December 1996
pp. 970-974
<p><b>Abstract</b>—One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into <it>objective</it...
 
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
Found in: ACM Transactions on Information Systems (TOIS)
By Alexander Tuzhilin, Hui Xiong, Qi Liu, Yong Ge
Issue Date:January 2014
pp. 1-31
Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data could be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendation...
     
Introduction to special section on intelligent mobile knowledge discovery and management systems
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Dr. Alexander Tuzhilin, Dr. Hui Xiong, Dr. Shashi Shekhar
Issue Date:December 2013
pp. 1-2
In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from s...
     
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems
Found in: Proceedings of the 7th ACM conference on Recommender systems (RecSys '13)
By Alexander Tuzhilin, Panagiotis Adamopoulos
Issue Date:October 2013
pp. 351-354
This paper proposes a novel method for estimating unknown ratings and recommendation opportunities and illustrates the practical implementation of the proposed approach by presenting a certain variation of the classical k-NN method in neighborhood-based co...
     
4th workshop on context-aware recommender systems (CARS 2012)
Found in: Proceedings of the sixth ACM conference on Recommender systems (RecSys '12)
By Alexander Tuzhilin, Ernesto William de Luca, Gediminas Adomavicius, Linas Baltrunas, Tim Hussein
Issue Date:September 2012
pp. 349-350
CARS 2012 builds upon the success of the three previous editions held in conjunction with the 3rd to 5th ACM Conferences on Recommender Systems from 2009 to 2011. The 1st CARS Workshop was held in New York, NY, USA, whereas Barcelona, Spain, was home of th...
     
Context-awareness in recommender systems: research workshop and movie recommendation challenge
Found in: Proceedings of the fourth ACM conference on Recommender systems (RecSys '10)
By Alan Said, Alexander Tuzhilin, Ernesto W. De Luca, Gediminas Adomavicius, Shlomo Berkovsky
Issue Date:September 2010
pp. 385-386
CARS and CAMRa were organized under the Context-awareness in Recommendation Systems special event and gathered academic researchers as well as industrial practitioners in a workshop and challenge.
     
An energy-efficient mobile recommender system
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Alexander Tuzhilin, Hui Xiong, Keli Xiao, Marco Gruteser, Michael Pazzani, Yong Ge
Issue Date:July 2010
pp. 899-908
The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (gre...
     
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Found in: Proceedings of the third ACM conference on Recommender systems (RecSys '09)
By Alexander Tuzhilin, Anto Pedone, Cosimo Palmisano, Michele Gorgoglione, Umberto Panniello
Issue Date:October 2009
pp. 265-268
Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the perform...
     
Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models
Found in: Proceedings of the third ACM conference on Recommender systems (RecSys '09)
By Akhmed Umyarov, Alexander Tuzhilin
Issue Date:October 2009
pp. 37-44
Previous work on using external aggregate rating information showed that this information can be incorporated in several different types of recommender systems and improves their performance. In this paper, we propose a more general class of methods that c...
     
Context-aware recommender systems
Found in: Proceedings of the 2008 ACM conference on Recommender systems (RecSys '08)
By Alexander Tuzhilin, Gediminas Adomavicius
Issue Date:October 2008
pp. 1-2
This tutorial will discuss vulnerabilities of collaborative recommendation algorithms: attacks that can be mounted against them and possible defenses that can be used. The tutorial will be of interest to researchers and practitioners in the area of collabo...
     
The long tail of recommender systems and how to leverage it
Found in: Proceedings of the 2008 ACM conference on Recommender systems (RecSys '08)
By Alexander Tuzhilin, Yoon-Joo Park
Issue Date:October 2008
pp. 1-2
The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and th...
     
Social networks: looking ahead
Found in: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '08)
By Alexander Tuzhilin, Andrew Tomkins, Christos Faloutsos, David Jensen, Gueorgi Kossinets, Jure Leskovec, Ravi Kumar
Issue Date:August 2008
pp. 5-6
By now, online social networks have become an indispensable part of both online and offline lives of human beings. A large fraction of time spent online by a user is directly influence by the social networks to which he/she belongs. This calls for a deeper...
     
Managing large collections of data mining models
Found in: Communications of the ACM
By Alexander Tuzhilin
Issue Date:February 2008
pp. 85-89
Data analysts and naive users alike in information-intensive organizations need automated ways to build, analyze, and maintain very large collections of data mining models.
     
Managing large collections of data mining models
Found in: Communications of the ACM
By Alexander Tuzhilin
Issue Date:February 2008
pp. 85-89
Data analysts and naive users alike in information-intensive organizations need automated ways to build, analyze, and maintain very large collections of data mining models.
     
Incorporating contextual information in recommender systems using a multidimensional approach
Found in: ACM Transactions on Information Systems (TOIS)
By Alexander Tuzhilin, Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen
Issue Date:January 2005
pp. 103-145
The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. Thi...
     
On the discovery of significant statistical quantitative rules
Found in: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04)
By Alexander Tuzhilin, Balaji Padmanabhan, Hong Zhang
Issue Date:August 2004
pp. 374-383
In this paper we study market share rules, rules that have a certain market share statistic associated with them. Such rules are particularly relevant for decision making from a business perspective. Motivated by market share rules, in this paper we consid...
     
Handling very large numbers of association rules in the analysis of microarray data
Found in: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '02)
By Alexander Tuzhilin, Gediminas Adomavicius
Issue Date:July 2002
pp. 396-404
The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for the analysis of such data. In this paper, we propose to use association rule ...
     
Querying multiple sets of discovered rules
Found in: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '02)
By Alexander Tuzhilin, Bing Liu
Issue Date:July 2002
pp. 52-60
Rule mining is an important data mining task that has been applied to numerous real-world applications. Often a rule mining system generates a large number of rules and only a small subset of them is really useful in applications. Although there exist some...
     
Small is beautiful: discovering the minimal set of unexpected patterns
Found in: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '00)
By Alexander Tuzhilin, Balaji Padmanabhan
Issue Date:August 2000
pp. 54-63
With over 800 million pages covering most areas of human endeavor, the World-wide Web is a fertile ground for data mining research to make a difference to the effectiveness of information search. Today, Web surfers access the Web through two dominant inter...
     
User profiling in personalization applications through rule discovery and validation
Found in: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '99)
By Alexander Tuzhilin, Gediminas Adomavicius
Issue Date:August 1999
pp. 377-381
This talk is an interim report on the 5 year plan launched in 1996 to provide a theoretical and computational foundation of Statistics for massive data sets. The plan coincided with the formation of AT&T Labs and the proposed research agenda of the In...
     
Templar: a knowledge-based language for software specifications using temporal logic
Found in: ACM Transactions on Information Systems (TOIS)
By Alexander Tuzhilin
Issue Date:January 1992
pp. 269-304
A software specification language Templar is defined in this article. The development of the language was guided by the following objectives: requirements specifications written in Templar should have a clear syntax and formal semantics, should be easy for...
     
Extending temporal logic to support high-level simulations
Found in: ACM Transactions on Modeling and Computer Simulation (TOMACS)
By Alexander Tuzhilin
Issue Date:January 1991
pp. 129-155
A high-level simulation language based on temporal logic is described. The language combines a large set of temporal tenses and a rich class of high-level modeling primitives. Also an implementation of the language interpreter is presented. Finally, a real...
     
On completeness of historical relational query languages
Found in: ACM Transactions on Database Systems (TODS)
By Albert Croker, Alexander Tuzhilin, James Clifford
Issue Date:March 1988
pp. 64-116
Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. These proposals have differed considerably in the way that the temporal dimension has been incorporated both i...
     
A semantic approach to correctness of concurrent transaction executions
Found in: Proceedings of the fourth ACM SIGACT-SIGMOD symposium on Principles of database systems (PODS '85)
By Alexander Tuzhilin, Paul G. Spirakis
Issue Date:March 1985
pp. 85-95
Relational algebras as developed by Codd and his followers are extended by noting an equivalence with functional languages. This leads to higher order relations, recursive definitions of relations, and the use of higher order relations as recursive data st...
     
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