Abstract
Since WEB2.0, more and more online communities began to use tag—words selected by users or generated by computer algorithms—to help people find or organize data resources. Unfortunately, the tags are generally in a random order without any importance or relevance in information, which seriously limit the effectiveness of these tags in tag-based applications. In this paper we present a tag ranking method which first computes the probability of each tag associated with a given book, and then adjust the probability as well as the tags’ order based on users’ tag-click behaviors. Then an initial strategy which provides a better initial probability is described to improve our method. Experimental results show that users’ tag-click behaviors can reflect the relevance between books and tags to some extent and our approach is both efficient and effective.
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Acknowledgment
This work was supported by the China Academic Digital Associative Library Project, the Special Funds for Key Program of National Science and Technology (Grant No. 2010ZX01042-002-003), the Program for Key Innovative Research Team of Zhejiang Province (Grant No. 2009R50009), the Program for Key Cultural Innovative Research Team of Zhejiang Province, the Fundamental Research Funds for the Central Universities, Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ13F020001) and the Opening Project of State Key Laboratory of Digital Publishing Technology.
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Zhang, P., Yao, L., Zhang, Y., Wei, B., Gao, C. (2014). A Probabilistic Method for Tag Ranking in Tagging System. In: Wen, Z., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54930-4_20
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DOI: https://doi.org/10.1007/978-3-642-54930-4_20
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