Abstract
IIR (Internet Information Retrieval) system searches important documents on the internet by measuring the importance of these documents. For this purpose, various ranking techniques are proposed and adopted. In this paper, we propose ReviewRank, a ranking technique for finding book reviews. With an increasing number of people buying books online, reviews of books written by other people have become more important. General ranking techniques measure the importance of documents based on references or quotations between documents through hyperlinks. However, they are not suitable for book reviews. In this paper, we analyze characteristics of the importance of book reviews based on voluntary participation or evaluation of people called as collective intelligence, and proposes measures for considering the importance. We also suggest a ranking algorithm which adopts ReviewRank for finding book reviews. Experimental results show that ReviewRank outperforms previous ranking techniques for both general IIR system and searching book reviews.
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Acknowledgments
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2012-0003740 and 2012-0000478).
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Ryang, H., Yun, U. (2013). Collective Intelligence Based Algorithm for Ranking Book Reviews. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_129
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DOI: https://doi.org/10.1007/978-94-007-5860-5_129
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