Abstract
This paper addresses the problem of detecting access to off-topic documents by exploiting user profiles. Existing methods usually store a few prototype off-topic documents as the profile and label their top nearest neighbors in the test set as suspects. This is based on the common assumption that nearby documents are from the same class. However, due to the inherent sparseness of high-dimensional space, a document and its nearest neighbors may not belong to the same class. To this end, we develop a hyperclique pattern based off-topic detection method for selecting which ones to label. Hyperclique patterns consider joint similarity among a set of objects instead of the traditional pairwise similarity. As a result, the objects from hypercliques are more reliable as seeds for classifying their neighbors. Indeed, our experimental results on real world document data favorably demonstrate the effectiveness of our technique over the existing methods in terms of detection precision.
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Hu, T., Xu, Q., Yuan, H., Hou, J., Qu, C. (2007). Hyperclique Pattern Based Off-Topic Detection. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_40
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DOI: https://doi.org/10.1007/978-3-540-72524-4_40
Publisher Name: Springer, Berlin, Heidelberg
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