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A Recommender System Based on Customer Reviews Mining

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

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Abstract

As e-commerce is becoming more and more popular, the number of different products reviews done by customer grows rapidly. The efficient method for automatic summarization of such reviews is required. The majority of existing approaches classify a review only whether the opinion is positive or negative. In the present paper we show how to extract product features from the set of the reviews to design feature based summaries of available opinions. These summaries, expressed in IF-set framework, are later used to recommend a customer the best product corresponding to his individual demands.

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References

  1. Atanassov, K.: Intuitionistic Fuzzy Sets: Theory and Applications. STUDFUZZ, vol. 35. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB Conference Santiago (1994)

    Google Scholar 

  3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  4. Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: KDD 2004 (2004)

    Google Scholar 

  5. Grzegorzewski, P.: Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the Hausdorff metric. Fuzzy Sets and Systems 148, 319–328 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  6. Grzegorzewski, P., Ładyżyński, P.P.: Comparing vague preferences in recommender systems. In: Proceedings of the EUROFUSE 2013 Workshop, pp. 149–157 (2013)

    Google Scholar 

  7. Grzegorzewski, P., Mrówka, E.: Flexible Querying via IF-sets. International Journal of Intelligent Systems 22, 587–597 (2007)

    Article  MATH  Google Scholar 

  8. Montes, S., Iglesias, T., Janiš, V., Montes, I.: A common framework for some comparison measures of IF-sets. In: IWIFSGN 2012, Warsaw (2012)

    Google Scholar 

  9. Jindal, N., Liu, B.: Opinion Spam and Analysis. In: WSDM 2008, Palo Alto (2008)

    Google Scholar 

  10. Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980)

    Article  Google Scholar 

  11. Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: ACL 2002 (2002)

    Google Scholar 

  12. Zhao, Y.: R and Data Mining: Examples and Case Studies. Elsevier (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Interdisciplinary PhD Studies, Polish Academy of Sciences, Jana Kazimierza 5, 01-248, Warsaw, Poland

    Paweł P. Ładyżyński

  2. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Przemysław Grzegorzewski

  3. Faculty of Mathematics and Computer Science, Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland

    Przemysław Grzegorzewski

Authors
  1. Paweł P. Ładyżyński
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  2. Przemysław Grzegorzewski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

  3. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Ładyżyński, P.P., Grzegorzewski, P. (2014). A Recommender System Based on Customer Reviews Mining. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_45

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  • DOI: https://doi.org/10.1007/978-3-319-07176-3_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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