A Design Framework for Event Recommendation in Novice Low-Literacy Communities
The proliferation of user-generated content (UGC) results in huge opportunities to explore event patterns. However, existing event recommendation systems primarily focus on advanced information technology users. Little work has been done to address novice and low-literacy users. The next billion users providing and consuming UGC are likely to include communities from developing countries who are ready to use affordable technologies for subsistence goals. Therefore, we propose a design framework for providing event recommendations to address the needs of such users. Grounded in information integration theory (IIT), our framework advocates that effective event recommendation is supported by systems capable of (1) reliable information gathering through structured user input, (2) accurate sense making through spatial-temporal analytics, and (3) intuitive information dissemination through interactive visualization techniques. A mobile pest management application is developed as an instantiation of the design framework. Our preliminary study suggests a set of design principles for novice and low-literacy users.
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user-generated content," 2009, pp. 29-36.
[2] Y. Wu, J. Wong, Y. Deng, and K. Chang, "An Exploration of Social
Media in Public Opinion Convergence: Elaboration Likelihood and
Semantic Networks on Political Events," presented at the Proceedings of
the International Conference on Social Computing and its Applications,
Sydney, Australia., 2011.
[3] A. Ghose, P. Ipeirotis, and B. Li, "Designing ranking systems for hotels
on travel search engines by mining user-generated and crowd-sourced content," 2011.
[4] P. A. Johnson, R. E. Sieber, N. Magnien, and J. Ariwi, "Automated web
harvesting to collect and analyse user-generated content for tourism,"2011.
[5] R. Herring, A. Hofleitner, S. Amin, T. Nasr, A. Khalek, P. Abbeel, and A.
Bayen, "Using mobile phones to forecast arterial traffic through statistical
learning," in 89th Annual Meeting of the Transportation Research Board,2010.
[6] M. Haklay and P. Weber, "OpenStreetMap: User-generated street maps,"
Pervasive Computing, IEEE, vol. 7, pp. 12-18, 2008.
[7] M. Kayaalp, T. Ozyer, and S. Ozyer, "A collaborative and content based event recommendation system integrated with data collection scrapers and services at a social networking site," 2009, pp. 113-118.
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and ubiquitous computing, vol. 7, pp. 70-79, 2003.
[9] B. Rao and L. Minakakis, "Evolution of mobile location-based services,"
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[10] I. A. Junglas and R. T. Watson, "Location-based services," Communications of the ACM, vol. 51, pp. 65-69, 2008.
[11] K. Chang, M. Gwee, Y. Deng, Y. Wu, X. Sha, and B. Tan,
"Location-Based and Mobile Services in the Fishing Industry of the Informal Economy," presented at the Posters of 11th IFIP Working Group 9.4 Conference, Kathmandu, Nepal, 2011.
[12] B. Shneiderman, "The eyes have it: a task by data type taxonomy for
information visualizations," in Visual Languages, 1996. Proceedings.,
IEEE Symposium on, 1996, pp. 336-343.
[13] J. G. Walls, G. R. Widmeyer, and O. A. El Sawy, "Building an
information system design theory for vigilant EIS," Information systems
research, vol. 3, pp. 36-59, 1992.
[14] R. Jain, V. Singh, and M. Gao, "Social Life Networks for Middle of the
Pyramid," 2010.
[15] A. Abbasi and H. Chen, "CyberGate: a design framework and system for
text analysis of computer-mediated communication," MIS Quarterly, vol.
32, pp. 811-837, 2008.
[16] E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, "Finding
high-quality content in social media," 2008, pp. 183-194.
[17] J. Van Dijck, "Users like you? Theorizing agency in user-generated
content," Media, Culture & Society, vol. 31, pp. 41-58, 2009.
[18] D. V. Morland, "Human factors guidelines for terminal interface design,"
Communications of the ACM, vol. 26, pp. 484-494, 1983.
[19] N. Cheung, V. Fung, Y. Chow, and Y. Tung, "Structured data entry of
clinical information for documentation and data collection," Studies in
health technology and informatics, pp. 609-613, 2001.
[20] W. R. Hogan and M. M. Wagner, "Accuracy of data in computer-based
patient records," Journal of the American Medical Informatics
Association, vol. 4, p. 342, 1997.
[21] A. M. Kaplan and M. Haenlein, "Users of the world, unite! The
challenges and opportunities of Social Media," Business horizons, vol. 53,
pp. 59-68, 2010.
[22] N. Pelekis, B. Theodoulidis, I. Kopanakis, and Y. Theodoridis,
"Literature review of spatio-temporal database models," The Knowledge
Engineering Review, vol. 19, pp. 235-274, 2004.
[23] P. Berkhin, "A survey of clustering data mining techniques," Grouping
multidimensional data, pp. 25-71, 2006.
[24] B. Sigurbjörnsson and R. Van Zwol, "Flickr tag recommendation based
on collective knowledge," in Proceedings of the 17th international
conference on World Wide Web, 2008, pp. 327-336.
[25] T. De Pessemier, T. Deryckere, and L. Martens, "Context aware
recommendations for user-generated content on a social network site," in
Proceedings of the seventh european conference on European interactive
television conference, 2009, pp. 133-136.
[26] J. Gong and P. Tarasewich, "Guidelines for handheld mobile device
interface design," 2004, pp. 3751-3756.
[27] D. Zhang and B. Adipat, "Challenges, methodologies, and issues in the
usability testing of mobile applications," International Journal of
Human-Computer Interaction, vol. 18, pp. 293-308, 2005.
[28] Information Integration Theory. Available:
http://www.cios.org/encyclopedia/persuasion/Finformation_integration_
1theory.htm
[29] J. H. Kim and K. P. Lee, "Cultural difference and mobile phone interface
design: Icon recognition according to level of abstraction," 2005, pp.
307-310.
[30] I. Medhi, S. Patnaik, E. Brunskill, S. Gautama, W. Thies, and K. Toyama,
"Designing mobile interfaces for novice and low-literacy users," ACM
Transactions on Computer-Human Interaction (TOCHI), vol. 18, p. 2,
2011.
[31] S. Chakrabarti, Martin Ester, Usama Fayyad, Johannes Gehrke, Jiawei
Han, Shinichi Morishita, Gregory Piatetsky-Shapiro, and W. Wang, "Data
mining curriculum: A proposal (Version 1.0)," in Intensive Working
Group of ACM SIGKDD Curriculum Committee, 2006.
[32] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. (1996) From Data
Mining to Knowledge Discovery in Databases. AI Magazine. 37-54.
[33] D. Birant and A. Kut, "ST-DBSCAN: An algorithm for clustering
spatial-temporal data," Data & Knowledge Engineering, vol. 60, pp.
208-221, 2007.
[34] A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, "A
clustering-based approach for discovering interesting places in
trajectories," 2008, pp. 863-868.
[35] D. Benyon, Designing Interactive Systems: a comprehensive guide to
HCI and interaction design (Second Edition): Pearson Education Limited,
2010.
[36] L. Aksoy, P. N. Bloom, N. H. Lurie, and B. Cooil, "Should
recommendation agents think like people?," Journal of Service Research,
vol. 8, pp. 297-315, 2006.
[37] B. Adipat, D. Zhang, and L. Zhou, "The effects of tree-view based
presentation adaptation on mobile web browsing," MIS Quarterly, vol.
35, pp. 99-122, 2011.
[1] L. Sarmento, P. Carvalho, M. J. Silva, and E. de Oliveira, "Automatic creation of a reference corpus for political opinion mining in
user-generated content," 2009, pp. 29-36.
[2] Y. Wu, J. Wong, Y. Deng, and K. Chang, "An Exploration of Social
Media in Public Opinion Convergence: Elaboration Likelihood and
Semantic Networks on Political Events," presented at the Proceedings of
the International Conference on Social Computing and its Applications,
Sydney, Australia., 2011.
[3] A. Ghose, P. Ipeirotis, and B. Li, "Designing ranking systems for hotels
on travel search engines by mining user-generated and crowd-sourced content," 2011.
[4] P. A. Johnson, R. E. Sieber, N. Magnien, and J. Ariwi, "Automated web
harvesting to collect and analyse user-generated content for tourism,"2011.
[5] R. Herring, A. Hofleitner, S. Amin, T. Nasr, A. Khalek, P. Abbeel, and A.
Bayen, "Using mobile phones to forecast arterial traffic through statistical
learning," in 89th Annual Meeting of the Transportation Research Board,2010.
[6] M. Haklay and P. Weber, "OpenStreetMap: User-generated street maps,"
Pervasive Computing, IEEE, vol. 7, pp. 12-18, 2008.
[7] M. Kayaalp, T. Ozyer, and S. Ozyer, "A collaborative and content based event recommendation system integrated with data collection scrapers and services at a social networking site," 2009, pp. 113-118.
[8] E. Kaasinen, "User needs for location-aware mobile services," Personal
and ubiquitous computing, vol. 7, pp. 70-79, 2003.
[9] B. Rao and L. Minakakis, "Evolution of mobile location-based services,"
Communications of the ACM, vol. 46, pp. 61-65, 2003.
[10] I. A. Junglas and R. T. Watson, "Location-based services," Communications of the ACM, vol. 51, pp. 65-69, 2008.
[11] K. Chang, M. Gwee, Y. Deng, Y. Wu, X. Sha, and B. Tan,
"Location-Based and Mobile Services in the Fishing Industry of the Informal Economy," presented at the Posters of 11th IFIP Working Group 9.4 Conference, Kathmandu, Nepal, 2011.
[12] B. Shneiderman, "The eyes have it: a task by data type taxonomy for
information visualizations," in Visual Languages, 1996. Proceedings.,
IEEE Symposium on, 1996, pp. 336-343.
[13] J. G. Walls, G. R. Widmeyer, and O. A. El Sawy, "Building an
information system design theory for vigilant EIS," Information systems
research, vol. 3, pp. 36-59, 1992.
[14] R. Jain, V. Singh, and M. Gao, "Social Life Networks for Middle of the
Pyramid," 2010.
[15] A. Abbasi and H. Chen, "CyberGate: a design framework and system for
text analysis of computer-mediated communication," MIS Quarterly, vol.
32, pp. 811-837, 2008.
[16] E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, "Finding
high-quality content in social media," 2008, pp. 183-194.
[17] J. Van Dijck, "Users like you? Theorizing agency in user-generated
content," Media, Culture & Society, vol. 31, pp. 41-58, 2009.
[18] D. V. Morland, "Human factors guidelines for terminal interface design,"
Communications of the ACM, vol. 26, pp. 484-494, 1983.
[19] N. Cheung, V. Fung, Y. Chow, and Y. Tung, "Structured data entry of
clinical information for documentation and data collection," Studies in
health technology and informatics, pp. 609-613, 2001.
[20] W. R. Hogan and M. M. Wagner, "Accuracy of data in computer-based
patient records," Journal of the American Medical Informatics
Association, vol. 4, p. 342, 1997.
[21] A. M. Kaplan and M. Haenlein, "Users of the world, unite! The
challenges and opportunities of Social Media," Business horizons, vol. 53,
pp. 59-68, 2010.
[22] N. Pelekis, B. Theodoulidis, I. Kopanakis, and Y. Theodoridis,
"Literature review of spatio-temporal database models," The Knowledge
Engineering Review, vol. 19, pp. 235-274, 2004.
[23] P. Berkhin, "A survey of clustering data mining techniques," Grouping
multidimensional data, pp. 25-71, 2006.
[24] B. Sigurbjörnsson and R. Van Zwol, "Flickr tag recommendation based
on collective knowledge," in Proceedings of the 17th international
conference on World Wide Web, 2008, pp. 327-336.
[25] T. De Pessemier, T. Deryckere, and L. Martens, "Context aware
recommendations for user-generated content on a social network site," in
Proceedings of the seventh european conference on European interactive
television conference, 2009, pp. 133-136.
[26] J. Gong and P. Tarasewich, "Guidelines for handheld mobile device
interface design," 2004, pp. 3751-3756.
[27] D. Zhang and B. Adipat, "Challenges, methodologies, and issues in the
usability testing of mobile applications," International Journal of
Human-Computer Interaction, vol. 18, pp. 293-308, 2005.
[28] Information Integration Theory. Available:
http://www.cios.org/encyclopedia/persuasion/Finformation_integration_
1theory.htm
[29] J. H. Kim and K. P. Lee, "Cultural difference and mobile phone interface
design: Icon recognition according to level of abstraction," 2005, pp.
307-310.
[30] I. Medhi, S. Patnaik, E. Brunskill, S. Gautama, W. Thies, and K. Toyama,
"Designing mobile interfaces for novice and low-literacy users," ACM
Transactions on Computer-Human Interaction (TOCHI), vol. 18, p. 2,
2011.
[31] S. Chakrabarti, Martin Ester, Usama Fayyad, Johannes Gehrke, Jiawei
Han, Shinichi Morishita, Gregory Piatetsky-Shapiro, and W. Wang, "Data
mining curriculum: A proposal (Version 1.0)," in Intensive Working
Group of ACM SIGKDD Curriculum Committee, 2006.
[32] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. (1996) From Data
Mining to Knowledge Discovery in Databases. AI Magazine. 37-54.
[33] D. Birant and A. Kut, "ST-DBSCAN: An algorithm for clustering
spatial-temporal data," Data & Knowledge Engineering, vol. 60, pp.
208-221, 2007.
[34] A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, "A
clustering-based approach for discovering interesting places in
trajectories," 2008, pp. 863-868.
[35] D. Benyon, Designing Interactive Systems: a comprehensive guide to
HCI and interaction design (Second Edition): Pearson Education Limited,
2010.
[36] L. Aksoy, P. N. Bloom, N. H. Lurie, and B. Cooil, "Should
recommendation agents think like people?," Journal of Service Research,
vol. 8, pp. 297-315, 2006.
[37] B. Adipat, D. Zhang, and L. Zhou, "The effects of tree-view based
presentation adaptation on mobile web browsing," MIS Quarterly, vol.
35, pp. 99-122, 2011.
@article{"International Journal of Business, Human and Social Sciences:62625", author = "Yimeng Deng and Klarissa T.T. Chang", title = "A Design Framework for Event Recommendation in Novice Low-Literacy Communities", abstract = "The proliferation of user-generated content (UGC) results in huge opportunities to explore event patterns. However, existing event recommendation systems primarily focus on advanced information technology users. Little work has been done to address novice and low-literacy users. The next billion users providing and consuming UGC are likely to include communities from developing countries who are ready to use affordable technologies for subsistence goals. Therefore, we propose a design framework for providing event recommendations to address the needs of such users. Grounded in information integration theory (IIT), our framework advocates that effective event recommendation is supported by systems capable of (1) reliable information gathering through structured user input, (2) accurate sense making through spatial-temporal analytics, and (3) intuitive information dissemination through interactive visualization techniques. A mobile pest management application is developed as an instantiation of the design framework. Our preliminary study suggests a set of design principles for novice and low-literacy users.
", keywords = "Event recommendation, iconic interface, information integration, spatial-temporal clustering, user-generated content, visualization techniques", volume = "6", number = "5", pages = "983-6", }
