Abstract
In this paper, the stable personal browsing patterns shown in Internet surfing are utilized to determine the users’ preference on specific content. To be more specific, they are used to calculate the so called implicit ratings. We performed an experiment on all possible combinations of the implicit indicators to pick out the most significant indicators— elements of user browsing patterns. A thorough analysis and comparison are carried out before four indicators are selected as the input of an Artificial Neural Network which is adopted to calculate the implicit ratings. The mechanism of the implicit rating calculation is integrated into an educational resource sharing system as a featured module and works well.
This work was supported by NSFC 70202008.
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Wang, S., Li, X., Liu, W. (2005). Implicit Rating – A Case Study. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_82
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DOI: https://doi.org/10.1007/11539117_82
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