Abstract
Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm –ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine–CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests.
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Guo, H., Zhang, J., Zhou, L. (2007). Classifying and Ranking: The First Step Towards Mining Inside Vertical Search Engines. In: Wagner, R., Revell, N., Pernul, G. (eds) Database and Expert Systems Applications. DEXA 2007. Lecture Notes in Computer Science, vol 4653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74469-6_23
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DOI: https://doi.org/10.1007/978-3-540-74469-6_23
Publisher Name: Springer, Berlin, Heidelberg
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