Application of Social Computing to Collaborative Web Search


Recently, e-learning has been paid much attention in the area of education. However, it is difficult for the low-achievement students to find out the key points and keywords while searching online articles. They usually cannot accurately obtain the website information even after searching for large amount of data in the Internet. Meanwhile, these low-achievement students often lack of the related prior knowledge to determine if the website is useful. Accordingly, they select those websites based on their disorderly instincts. In this work, an intelligent collaborative web search assistance platform is proposed. A group grading module is presented to derive three parameters that are used to calculate the ranking of each website via the Support Vector Regression method. The effects of website ranking shorten the searching processes, and the learners can thus have more time to focus on comprehending the contents of the recommended website. The experimental results reveal that the proposed algorithm can effectively guide learners to search the appropriate website; accordingly, the target of self-learning assistance can be achieved and the learning performance of the students is enhanced.

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Huang, C. , Chen, H. , Chang, S. and Chien, S. (2015) Application of Social Computing to Collaborative Web Search. Open Journal of Social Sciences, 3, 28-33. doi: 10.4236/jss.2015.39005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Prekop, P (2002) A Qualitative Study of Collaborative Information Seeking. Journal of Documentation, 58, 533-547.
[2] Hyldega, J. (2006) Collaborative Information Behaviour—Exploring Kuhlthau’s Information Process Model in a Group-Based Educational Setting. Information Processing & Management, 42, 276-298.
[3] Fidel, R., Pejtersen, A.M., Cleal, B. and Bruce, H. (2004) A Multi-dimensional Approach to the Study of Human-Information Interaction: A case Study of Collaborative Information Retrieval. Journal of the American Society for Information Science and Technology, 55, 939-953.
[4] Lieberman, H., van Dyke, N. and Vivacqua, A. (1999) Let’s Browse: A Col-laborative Browsing Agent. Knowledge- Based Systems, 12, 427-431.
[5] Romano, N.C., Roussinovm D., Nunamakerm J.F. and Chen, H. (1999) Collaborative Information Retrieval Environment: Integration of Information Retrieval with Group Support Systems. Proceedings of the 32nd Hawaii International Conference on Systemsciences, IEEE Computer Society Press, Los Alamitos.
[6] Chirag, S., Robert, C. and Preben, H. (2014) Collaborative Informa-tion Seeking. Computer, 47, 22-25.
[7] Morris, M.R. (2008) A Survey of Collaborative Web Search Practices. Pro-ceedings of CHI 2008, 1657-1660.
[8] Drucker, H., Chris, J., Burges, C., Kaufman, L., Smola, A. and Vapnik, V. (1997) Support Vector Regression Machines. Advances in Neural Information Processing Systems, 9, 155-161.
[9] Wu, C.H., Ho, J.M. and Lee, D.T. (2004) Travel-Time Prediction with Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems, 5, 276-281.
[10] Liu, X., Lu, W.C., Jin, S.G., Li, Y.W. and Chen, N.Y. (2006) Support Vector Regression Applied to Materials Optimization of Sialon Ceramics. Chemometrics and Intelligent Laboratory Systems, 82, 8-14.

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