Volume : 2, Issue : 11, NOV 2016
CAN SEARCH INTERFACE HELP USER?
Fataneh Vahabi
Abstract
Today, we discuss control of user and predication of system in the exploratory search. By directing a search engine, problems such as controllability and predictability may arise. We can reduce problems by seeing the effects and by interpreting user's actions. A user study showed small improvements in user acceptance, perceived usefulness and task performance.
Keywords
feedback, interface, users.
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Article No : 12
Number of Downloads : 928
References
1. Robinson, A.C., Roth, R.E., MacEachren, A.M. Designing a web-Based learning portal for geographic visualization and analysis in public health. Heal. Inf. J. 2011;17:191–208 2. Curtis, J.R., Westfall, A.O., Allison, J., Becker, A., Melton, M.E., Freeman, A. et al, Challenges in improving the quality of osteoporosis care for long-term glucocorticoid users: a prospective randomized trial. Arch. Intern. Med. 2007;167:591–596 3. Itri, J.N., Jones, L.P., Kim, W., Boonn, W.W., Kolansky, A.S., Hilton, S. et al, Developing an automated database for monitoring ultrasound- and computed tomography-guided procedure complications and diagnostic yield. J. Digit Imaging. 2014;27:270–279 4. Ratwani, R.M., Fong, and A. connecting the dots: leveraging visual analytics to make sense of patient safety event reports. J. Am. Med. Inform. Assoc. 2015;22:312–317 5. A.L. Hartzler, B.C., Fey, D.R. Flum, Integrating Patient-Reported Outcomes into Spine Surgical Care through Visual Dashboards: Lessons Learned from Human-Centered Design Integrating Patient-Reported Outcomes into Spine Surgical Care through, 3 (2015) 3–13. doi: 10.13063/2327-9214.1133. 6. Brown, B., Jameson, D., Daker-White, G., Buchan, I., Ivers, N., Peek, N. et al, A meta-synthesis of findings from qualitative studies of audit and feedback interventions. PROSPERO Int. Prospect. Regist. Syst. Rev. 2015. 7. Mainz, J. Defining and classifying clinical indicators for quality improvement. Int. J. Qual. Health Care. 2003;15:523–530. 8. Schwartz AB. Cortical neural prosthetics. Annual Review of Neuroscience.2004 Jul;27:487–507. doi: 10.1146/annurev.neuro.27.070203.144233. pmid:15217341 9. Carmena JM. Advances in Neuroprosthetic Learning and Control. PLoS Biol.2013 05;11(5):e1001561. doi: 10.1371/journal.pbio.1001561. pmid:23700383 10. Lebedev M. Brain-machine interfaces: an overview. Translational Neuroscience. 2014 Mar;5(1):99–110. doi: 10.2478/s13380-014-0212-z. 11. Taylor DM, Tillery SIH, Schwartz AB. Direct Cortical Control of 3D Neuroprosthetic Devices. Science. 2002 Jun;296:1829–1832. doi: 10.1126/science.1070291. pmid:12052948 12. Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Instant neural control of a movement signal. Nature. 2002 Mar;416:141–142. doi: 10.1038/416141a. pmid:11894084
13. Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, et al. Learning to Control a Brain-Machine Interface for Reaching and Grasping by Primates. PLoS Biol. 2003;1(2):e42. doi:10.1371/journal.pbio.0000042. pmid:14624244 14. Musallam S, Corneil BD, Greger B, Scherberger H, Andersen RA. Cognitive control signals for neural prosthetics. Science. 2004 Jul;305:258–262. doi: 10.1126/science.1097938. pmid:15247483
