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Michael Ekstrand

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Publication statistics

Pub. period:2011-2012
Pub. count:4
Number of co-authors:10

Co-authors

Number of publications with 3 favourite co-authors:

John Riedl:2
Daniel Kluver:1
Tien T. Nguyen:1

Productive colleagues

Michael Ekstrand's 3 most productive colleagues in number of publications:

John Riedl:61
Tovi Grossman:42
Wei Li:23

Calendar

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

May 29

Boston UXPA Usability and UX Conf. 2013

May 25

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Michael Ekstrand

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Publications by Michael Ekstrand (bibliography)

2012

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Kluver, Daniel, Nguyen, Tien T., Ekstrand, Michael, Sen, Shilad and Riedl, John (2012): How many bits per rating?. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 99-106.

“Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.”

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Ekstrand, Michael and Riedl, John (2012): When recommenders fail: predicting recommender failure for algorithm selection and combination. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 233-236.

“Hybrid recommender systems -- systems using multiple algorithms together to improve recommendation quality -- have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the relative performance of recommenders varies by circumstance and attempt to optimize each item score to maximize the strengths of the component recommenders. Less attention, however, has been paid to understanding what these strengths and failure modes are. Understanding what causes particular recommenders to fail will facilitate better selection of the component recommenders for future hybrid systems and a better understanding of how individual recommender personalities can be harnessed to improve the recommender user experience. We present an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.”

2011

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Willemsen, Martijn, Bollen, Dirk and Ekstrand, Michael (2011): UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 395-396.

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Ekstrand, Michael, Li, Wei, Grossman, Tovi, Matejka, Justin and Fitzmaurice, George (2011): Searching for software learning resources using application context. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 195-204.

“Users of complex software applications frequently need to consult documentation, tutorials, and support resources to learn how to use the software and further their understanding of its capabilities. Existing online help systems provide limited context awareness through "what's this?" and similar techniques. We examine the possibility of making more use of the user's current context in a particular application to provide useful help resources. We provide an analysis and taxonomy of various aspects of application context and how they may be used in retrieving software help artifacts with web browsers, present the design of a context-aware augmented web search system, and describe a prototype implementation and initial user study of this system. We conclude with a discussion of open issues and an agenda for further research.”

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