Publication statistics

Pub. period:2002-2012
Pub. count:12
Number of co-authors:19



Co-authors

Number of publications with 3 favourite co-authors:

Dieter Fox:2
Anthony LaMarca:2
Omar Alonso:2

 

 

Productive colleagues

Matthew Lease's 3 most productive colleagues in number of publications:

W. Bruce Croft:124
James Allan:63
Anthony LaMarca:37
 
 
 
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Matthew Lease

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Publications by Matthew Lease (bibliography)

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2012
 
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Ryu, Hohyon, Lease, Matthew and Woodward, Nicholas (2012): Finding and exploring memes in social media. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media 2012. pp. 295-304.

Online critical literacy challenges readers to recognize and question how online textual information has been shaped by its greater context. While comparing information from multiple sources provides a foundation for such awareness, keeping pace with everything being written is a daunting proposition, especially for the casual reader. We propose a new form of technological assistance for critical literacy which automatically discovers and displays underlying memes: ideas represented by similar phrases which occur across diżerent information sources. By surfacing these memes to users, we create a rich hypertext representation in which underlying memes can be explored in context. Given the vast scale of social media, we describe a highly-scalable system architecture designed for MapReduce distributed computing. To validate our approach, we report on use of our system to discover and browse memes in a 1.5 TB collection of crawled social media. Our primary contributions include: 1) a novel technological approach and hypertext browsing design for supporting critical literacy; and 2) a highly-scalable system architecture for meme discovery, providing a solid foundation for further system extensions and refinements.

© All rights reserved Ryu et al. and/or ACM Press

 
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Jung, Hyun Joon and Lease, Matthew (2012): Inferring missing relevance judgments from crowd workers via probabilistic matrix factorization. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1095-1096.

In crowdsourced relevance judging, each crowd worker typically judges only a small number of examples, yielding a sparse and imbalanced set of judgments in which relatively few workers influence output consensus labels, particularly with simple consensus methods like majority voting. We show how probabilistic matrix factorization, a standard approach in collaborative filtering, can be used to infer missing worker judgments such that all workers influence output labels. Given complete worker judgments inferred by PMF, we evaluate impact in unsupervised and supervised scenarios. In the supervised case, we consider both weighted voting and worker selection strategies based on worker accuracy. Experiments on crowd judgments from the 2010 TREC Relevance Feedback Track show promise of the PMF approach merits further investigation and analysis.

© All rights reserved Jung and Lease and/or ACM Press

 
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Shukla, Shilpa, Lease, Matthew and Tewari, Ambuj (2012): Parallelizing ListNet training using spark. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 1127-1128.

As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy. Exploiting independence of "summation form" computations, we show how each iteration in ListNet gradient descent can benefit from parallel execution. We seek to draw the attention of the IR community to use Spark, a newly introduced distributed cluster computing system, for reducing training time of iterative learning to rank algorithms. Unlike MapReduce, Spark is especially suited for iterative and interactive algorithms. Our results show near linear reduction in ListNet training time using Spark on Amazon EC2 clusters.

© All rights reserved Shukla et al. and/or ACM Press

 
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Lease, Matthew and Alonso, Omar (2012): Crowdsourcing for search evaluation and social-algorithmic search. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. p. 1180.

The first computers were people. Today, Internet-based access to 24/7 online human crowds has led to a renaissance of research in human computation and the advent of crowdsourcing. These new opportunities have brought a disruptive shift to research and practice for how we build intelligent systems today. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see human computation being integrated into the systems themselves, operating in concert with automation. This tutorial introduces opportunities and challenges of human computation and crowdsourcing, particularly for search evaluation and developing hybrid search solutions that integrate human computation with traditional forms of automated search. We review methodology and findings of recent research and survey current generation crowdsourcing platforms now available, analyzing methods, potential, and limitations across platforms.

© All rights reserved Lease and Alonso and/or ACM Press

2011
 
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Tian, Aibo and Lease, Matthew (2011): Active learning to maximize accuracy vs. effort in interactive information retrieval. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 145-154.

We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial document ranking, user interaction with the system produces relevance feedback (RF) which the system then uses to revise the ranking. This interactive process repeats until the user terminates the search. To maximize accuracy relative to user effort, we propose an active learning strategy. At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it. Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user. Evaluation on Robust04 under noisy feedback and on LETOR collections further demonstrate the effectiveness of active learning, as well as value of negative feedback in this task scenario.

© All rights reserved Tian and Lease and/or ACM Press

 
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Kumar, Abhimanu and Lease, Matthew (2011): Learning to rank from a noisy crowd. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1221-1222.

We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classification [5, 3], and aggregating graded relevance judgments from reliable experts for ranking [7]. To model varying performance of the crowd, we simulate annotation noise with varying magnitude and distributional properties. Evaluation on three LETOR test collections reveals a striking trend contrary to prior studies: single labeling outperforms consensus methods in maximizing learner accuracy relative to annotator eżort. We also see surprising consistency of the learning curve across noise distributions, as well as greater challenge with the adversarial case for multi-class labeling.

© All rights reserved Kumar and Lease and/or ACM Press

 
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Alonso, Omar and Lease, Matthew (2011): Crowdsourcing for information retrieval: principles, methods, and applications. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1299-1300.

Crowdsourcing has emerged in recent years as a promising new avenue for leveraging today's digitally-connected, diverse, distributed workforce. Generally speaking, crowdsourcing describes outsourcing of tasks to a large group of people instead of assigning such tasks to an in-house employee or contractor. Crowdsourcing platforms such as Amazon Mechanical Turk and CrowdFlower have gained particular attention as active online market places for reaching and tapping into this still largely under-utilized workforce. Crowdsourcing also offers intriguing new opportunities for accomplishing different kinds of tasks or achieving broader participation than previously possible, as well as completing standard tasks more accurately in less time and at lower cost. Unlocking the potential of crowdsourcing in practice, however, requires a tri-partite understanding of principles, platforms, and best practices. We will introduce the opportunities and challenges of crowdsourcing while discussing the three issues above. This will provide a basic foundation to begin crowdsourcing in the context of one's own particular tasks.

© All rights reserved Alonso and Lease and/or ACM Press

 
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Zhou, Yongyi, Broussard, Ramona and Lease, Matthew (2011): Mobile options for online public access catalogs. In: Proceedings of the 2011 iConference 2011. pp. 598-605.

As mobile devices continue to proliferate and become more tightly integrated with our daily activities, a number of libraries have begun deploying customized mobile Web portals and applications to promote accessibility for patrons. Despite rapid growth of these mobile solutions, their novelty has meant relatively little is known about the alternatives and tradeoffs in designing for mobile access to libraries. To investigate these issues, we describe three complementary approaches. First, we report on a content analysis comparing mobile solutions offered by 22 institutions. Next, we present a user survey of university students, staff, and faculty regarding their uses and needs for mobile catalog access. Based on these findings, we describe a prototype mobile application we built to provide mobile access to our own university's library catalog. Overall, we find that libraries have several tiered options that make it simple to provide basic functionality with relatively little effort and deliver a significantly improved user experience in comparison to relying on traditional browser-based solutions.

© All rights reserved Zhou et al. and/or ACM Press

2009
 
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Lease, Matthew (2009): An improved markov random field model for supporting verbose queries. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2009. pp. 476-483.

Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation. In this paper, we show retrieval accuracy with Metzler and Croft's Markov random field (MRF) approach can be similarly improved via supervised learning. While the original MRF method estimates a parameter for each of its three feature classes from data, parameters within each class are set via a uniform weighting scheme adopted from the standard unigram. We conjecture greater MRF retrieval accuracy should be possible by better estimating within-class parameters, particularly for verbose queries employing natural language terms. Retrieval experiments with these queries on three TREC document collections show our improved MRF consistently out-performs both the original MRF and supervised unigram baselines. Additional experiments using blind-feedback and evaluation with optimal weighting demonstrate both the immediate value and further potential of our method.

© All rights reserved Lease and/or his/her publisher

 
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Lease, Matthew, Allan, James and Croft, W. Bruce (2009): Regression Rank: Learning to Meet the Opportunity of Descriptive Queries. In: Boughanem, Mohand, Berrut, Catherine, Mothe, Josiane and Soulť-Dupuy, Chantal (eds.) Advances in Information Retrieval - 31th European Conference on IR Research - ECIR 2009 April 6-9, 2009, 2009, Toulouse, France. pp. 90-101.

2002
 
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LaMarca, Anthony, Brunette, Waylon, Koizumi, David, Lease, Matthew, Sigurdsson, Stefan B., Sikorski, Kevin, Fox, Dieter and Borriello, Gaetano (2002): PlantCare: An Investigation in Practical Ubiquitous Systems. In: Borriello, Gaetano and Holmquist, Lars Erik (eds.) UbiComp 2002 Ubiquitous Computing - 4th International Conference September 29 - October 1, 2002, GŲteborg, Sweden. pp. 316-332.

 
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LaMarca, Anthony, Brunette, Waylon, Koizumi, David, Lease, Matthew, Sigurdsson, Stefan B., Sikorski, Kevin, Fox, Dieter and Borriello, Gaetano (2002): Making Sensor Networks Practical with Robots. In: Mattern, Friedemann and Naghshineh, Mahmoud (eds.) Pervasive 2002 - Pervasive Computing, First International Conference August 26-28, 2002, ZŁrich, Switzerland. pp. 152-166.

 
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Changes to this page (author)

23 Nov 2012: Modified
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03 Nov 2010: Modified
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30 May 2009: Added

Page Information

Page maintainer: The Editorial Team
URL: http://www.interaction-design.org/references/authors/matthew_lease.html

Publication statistics

Pub. period:2002-2012
Pub. count:12
Number of co-authors:19



Co-authors

Number of publications with 3 favourite co-authors:

Dieter Fox:2
Anthony LaMarca:2
Omar Alonso:2

 

 

Productive colleagues

Matthew Lease's 3 most productive colleagues in number of publications:

W. Bruce Croft:124
James Allan:63
Anthony LaMarca:37
 
 
 
Jul 31

Design is the art of gradually applying constraints until only one solution remains.

-- Unknown

 
 

Featured chapter

Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess

User Experience and Experience Design !

 
 

Our Latest Books

Kumar and Herger 2013: Gamification at Work: Designing Engaging Business Software...
by Janaki Mythily Kumar and Mario Herger

 
Start reading

Whitworth and Ahmad 2013: The Social Design of Technical Systems: Building technologies for communities...
by Brian Whitworth and Adnan Ahmad

 
Start reading

Soegaard and Dam 2013: The Encyclopedia of Human-Computer Interaction, 2nd Ed....
by Mads Soegaard and Rikke Friis Dam

 
Start reading
 
 

Help us help you!