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Effective Ranking Techniques for Book Review Retrieval Based on the Structural Feature

  • Heungmo Ryang
  • Unil Yun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6935)

Abstract

Buying products online is becoming a way of life for people. Buying books online is also one of a way of life. With an increasing number of people buying books online, reviews of books written by other people are becoming more important. However, review systems which are serviced by websites of online bookstores or collected reviews have limited browsing and searching functions. Moreover, search engine for reviews posted such websites doesn’t exist. Therefore, retrieval system which is proper for searching reviews has been required. To the best of our knowledge, this is the first work that studies ranking techniques for book review retrieval. In this paper, we propose ranking techniques for book review retrieval based on the structural features. We show that our ranking techniques outperform previous a ranking technique for theinternet information retrieval on searching for reviews.

Keywords

book review ranking technique information retrieval 

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References

  1. 1.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford InfoLab. (1999)Google Scholar
  2. 2.
    Ramakrishna, V., Vagelis, H., Louiqa, R., Vidal, M.-E., Luis, D.I., Héctor, R.D.: Flexible and Efficient Querying and Ranking on Hyperlinked Data Sources. In: EDBT, pp. 553–564. Saint-Petersburg (2009)Google Scholar
  3. 3.
    Alyguliev, R.M.: Analysis of Hyperlinks and the Ant Algorithm for Calculating the Ranks of Web Pages. ACCS 41(1), 44–53 (2007)Google Scholar
  4. 4.
    Aktas, M.S., Nacar, M.A., Menczer, F.: Using Hyperlink Features to Personalize Web Search. In: WebKDD, Seattle (2004)Google Scholar
  5. 5.
    Apostolos, K., Martha, S., Iraklis, V.: BLOGRANK: Ranking Weblogs Based On Connectivity and Similarity Features, CoRR (2009)Google Scholar
  6. 6.
    Tayebi, M.A., Hashemi, S.M., Mohames, A.: B2Rank: An Algorithm for Ranking Blogs Based on Behavioral Features. In: Web Intelligence, pp. 104–107. Silicon Valley (2007)Google Scholar
  7. 7.
    Yajuan, D., Long, J., Tao, Q., Ming, Z., Heung-Yeung, S.: An Empirical Study on Learning to Rank of Tweets. In: COLING, Beijing, pp. 295–303 (2010)Google Scholar
  8. 8.
    Michael, J.W., Uri, S., Dan, H., Junghoo, C.: Topical semantics of twitter links. In: WSDM, Hong Kong, pp. 327–336 (2011)Google Scholar
  9. 9.
    Rinkesh, N., Ankur, T., Martine, D.C.: Ranking Approaches for Microblog Search. In: Web Intelligence, Toronto, pp. 153–157 (2010)Google Scholar
  10. 10.
    Meeyoung, C., Hamed, H., Fabrício, B., Krishna, P.G.: Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM, Washington (2010)Google Scholar
  11. 11.
    Daniel, G.A.: Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms, CoRR (2010)Google Scholar
  12. 12.
    Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: WSDM, New York, pp. 261–270 (2010)Google Scholar
  13. 13.
    Liangjie, H., Brian, D.D.: Empirical Study of Topic Modeling in Twitter. SOMA (2010)Google Scholar
  14. 14.
    Matthew, M., Sofus, A.M.: Discovering users’ topics of interest on twitter: a first look. AND, Toronto, pp. 73–80 (2010)Google Scholar
  15. 15.
    Akiko, N.A.: An Information-theoretic Perspective of Tf-idf Measures. IPM 39(1), 45–65 (2003)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Search engine for internet information, http://www.google.com
  17. 17.
    Online bookstore, http://www.amazon.com
  18. 18.
    Social network for readers, http://www.goodreads.com

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Heungmo Ryang
    • 1
  • Unil Yun
    • 1
  1. 1.Chungbuk National UniversityRepublic of Korea

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