%0 Conference Paper %1 citeulike:14256259 %A Wong, David %A Faridani, Siamak %A Bitton, Ephrat %A Hartmann, Björn %A Goldberg, Ken %B CHI '11 Extended Abstracts on Human Factors in Computing Systems %C New York, NY, USA %D 2011 %I ACM %K diversity information-visualization recommender %P 1471--1476 %R 10.1145/1979742.1979793 %T The Diversity Donut: Enabling Participant Control over the Diversity of Recommended Responses %U http://dx.doi.org/10.1145/1979742.1979793 %X Most online discussion interfaces organize textual responses using linear lists. Such lists do not scale to the number of responses and cannot convey the diversity of the participants who have contributed. The Opinion Space system is designed to address these issues. In this paper, we augment Opinion Space with two features. The first is a new user interface tool and recommendation system: the Diversity Donut (Figure 1). While the Diversity Donut did not establish a statistical advantage over other recommendation methods, participant self-reported data suggested that participants found the Diversity Donut to yield the most diverse set of comments. The second contribution is a new dimensionality reduction technique in Opinion Space: Canonical Correlation Analysis (CCA). Our analysis suggests that CCA is a better algorithm for opinion visualization than Principal Component Analysis (PCA). %@ 978-1-4503-0268-5

@inproceedings{citeulike:14256259, abstract = {{Most online discussion interfaces organize textual responses using linear lists. Such lists do not scale to the number of responses and cannot convey the diversity of the participants who have contributed. The Opinion Space system is designed to address these issues. In this paper, we augment Opinion Space with two features. The first is a new user interface tool and recommendation system: the Diversity Donut (Figure 1). While the Diversity Donut did not establish a statistical advantage over other recommendation methods, participant self-reported data suggested that participants found the Diversity Donut to yield the most diverse set of comments. The second contribution is a new dimensionality reduction technique in Opinion Space: Canonical Correlation Analysis (CCA). Our analysis suggests that CCA is a better algorithm for opinion visualization than Principal Component Analysis (PCA).}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Wong, David and Faridani, Siamak and Bitton, Ephrat and Hartmann, Bj\"{o}rn and Goldberg, Ken}, biburl = {https://www.bibsonomy.org/bibtex/24b1f20c8f9bdfef245d10d33cab4cfeb/aho}, booktitle = {CHI '11 Extended Abstracts on Human Factors in Computing Systems}, citeulike-article-id = {14256259}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1979793}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1979742.1979793}, doi = {10.1145/1979742.1979793}, interhash = {87c677a83e0751b76f36884ae9df1c99}, intrahash = {4b1f20c8f9bdfef245d10d33cab4cfeb}, isbn = {978-1-4503-0268-5}, keywords = {diversity information-visualization recommender}, location = {Vancouver, BC, Canada}, pages = {1471--1476}, posted-at = {2017-01-17 14:28:19}, priority = {2}, publisher = {ACM}, series = {CHI EA '11}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{The Diversity Donut: Enabling Participant Control over the Diversity of Recommended Responses}}, url = {http://dx.doi.org/10.1145/1979742.1979793}, year = 2011 }