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Improving Recommendation Based on Features’ Co-occurrence Effects in Collaborative Tagging Systems

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Web Technologies and Applications (APWeb 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7235))

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Abstract

Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users’ attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user’s favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.

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© 2012 Springer-Verlag Berlin Heidelberg

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Han, H., Cai, Y., Shao, Y., Li, Q. (2012). Improving Recommendation Based on Features’ Co-occurrence Effects in Collaborative Tagging Systems. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_61

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  • DOI: https://doi.org/10.1007/978-3-642-29253-8_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29252-1

  • Online ISBN: 978-3-642-29253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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