Advertisement

A Method for Measuring Similarity of Books: A Step Towards an Objective Recommender System for Readers

  • Adam WojciechowskiEmail author
  • Krzysztof Gorzynski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9561)

Abstract

In the paper we propose a method for book comparison based on graphical radar chart intersection area method. The method was designed as a universal tool and its most important parameter is document feature vector (DFV), which defines a set of text descriptors used to measure particular properties of analyzed text. Numerical values of the DFV that define book characteristic are stretched on radar chart and intersection area drawn for two books is interpreted as a measure of bilateral similarity in sense of defined DFV. Experiment conducted on relatively simple definition of the DFV gave promising results in recognition of books’ similarity (in sense of author and literature domain). Such an approach may be used for building a recommender system for readers willing to select a book matching their preferences recognized by objective properties of a reference book.

Keywords

Book content comparison Text descriptors Radar chart intersection area method Recommender system 

References

  1. Gorzynski, K.: System for analyzing publications to estimate similarity degree. Master thesis under supervision of Adam Wojciechowski. Poznan University of Technology, Poland (in Polish) (2013)Google Scholar
  2. Hamilton-Locke, Inc.: Methods for contextual discovery and analysis (2002). Retrieved http://www.hamilton-locke.com/support_files/Methods_for_Contextual_Discovery_and_Analysis.pdf. Accessed Nov 2013
  3. Kincaid, J.P., Fishburne, R.P., Rogers, R.L., Chissom, B.S.: Derivation of New Readability Formulas (Automated Readability Index, Fog Count, and Flesch Reading Ease formula) for Navy Enlisted Personnel. Research Branch Report 8–75. Chief of Naval Technical Training: Naval Air Station Memphis (1975)Google Scholar
  4. Lee, J., Park, D.-H., Han, I.I.: The effect of negative online consumer reviews on product attitude: an information processing view. Electron. Commer. Res. Appl. 7(1), 341–352 (2008). ElsevierCrossRefGoogle Scholar
  5. Li, Y.-M., Wu, C.-T., Lai, C.-Y.: A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis. Support Syst. 55(3), 740–752 (2013). ElsevierCrossRefGoogle Scholar
  6. Miranda-Garcia, A., Calle-Martin, J.: Yule’s Characteristic K Revisited. Lang. Resour. Eval. 39(4), 287–294 (2006). SpringerCrossRefGoogle Scholar
  7. Schafer, J.B., Konstan, J.A., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of the First ACM Conference on Electronic Commerce (EC-1999), pp. 158–166. ACM, Denver (1999)Google Scholar
  8. Susmaga, R., Maslowska, I., Budzynska, L.: The concept of topological information in text representation. Found. Comput. Decis. Sci. 36(1), 57–78 (2011). Poznan University of Technology, PolandGoogle Scholar
  9. Wilbur, W.J., Sirotkin, K.: The automatic identification of stop words. J. Inf. Sci. 18(1), 45–55 (1992)CrossRefGoogle Scholar
  10. Wojciechowski, A.: Supporting social networks by event-driven mobile notification services. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part I. LNCS, vol. 4805, pp. 398–406. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. Yule, G.U.: The Statistical Study of Literary Vocabulary. Cambridge University Press, Cambridge (1944)Google Scholar
  12. Math Open Reference, Area of Polygon (coordinate geometry). http://www.mathopenref.com/coordpolygonarea.html. Accessed Sept 2015
  13. Line-line intersection. https://en.wikipedia.org/wiki/Line–line_intersection. Accessed Sept 2015

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

Personalised recommendations