Abstract
Recently, with the rise of crowdsourcing, the concept that problems can only be solved by known experts has gradually been replaced. More and more people try to solve the problems via crowdsourcing, with not only efficiency but also inexpensiveness. In this research, we develop a nearby expert discovering mechanism by combining mobile intelligence and social community, and taking crowd wisdom, context, and social impacts into considered. The proposed system allows users to find nearby people whom have certain expertise in handling with difficult problems in real-time via a mobile device.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Boujarwah, F., Abowd, G. Arriaga, R. (2012). Socially computed scripts to support social problem solving skills. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM.
Bozzon, A., Brambilla, M., Ceri, S., Silvestri, M., Vesci, G. (2013). Choosing the right crowd: expert finding in social networks. Proceedings of the 16th International Conference on Extending Database Technology, ACM.
Davis, D. (2012). “More smartphone owners rely on location-based services, but fewer check in.” from http://www.internetretailer.com/2013/09/12/more-smartphone-owners-rely-location-based-services.
Eagle, N. (2009). txteagle: Mobile crowdsourcing. Internationalization, Design and Global Development, Springer: 447-456.
Hall, A., Wellman, B. (1985). “Social networks and social support.”
Howe, Jeff (June 2, 2006). “Crowdsourcing: A Definition”. Crowdsourcing Blog. Retrieved January 2, 2013.
Li, Y.-M., Lee, Y.-L., Lien, N.-J. (2012). “Online social advertising via influential endorsers.” International Journal of Electronic Commerce 16(3): 119-154.
Liu, D.-R., Chen, Y.-H., Kao, W.-C., Wang, H.-W. (2013). “Integrating expert profile, reputation and link analysis for expert finding in question-answering websites.” Information Processing & Management 49(1): 312-329.
Yang, D., Zhang, D., Yu, Z., Yu, Z. (2013). Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, ACM.
Zheng, Y., Capra, L., Wolfson, O., Yang, H. (2014). “Urban computing: concepts, methodologies, and applications.” ACM Transactions on Intelligent Systems and Technology (TIST) 5(3): 38.
Ziegler, C.-N., Golbeck, J. (2007). “Investigating interactions of trust and interest similarity.” Decision support systems 43(2): 460-475.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hwang, TK., Li, YM., Jin, BH. (2016). A Nearby Expert Discovering Mechanism: For Social Support. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-31232-3_89
Download citation
DOI: https://doi.org/10.1007/978-3-319-31232-3_89
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31231-6
Online ISBN: 978-3-319-31232-3
eBook Packages: EngineeringEngineering (R0)