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Aizu-BUS: Need-Based Book Recommendation Using Web Reviews and Web Services

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4777))

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Abstract

Presently, there are three approaches that constitute recommender systems: collaborative filtering, content-based approach and a hybrid system. This paper proposes a complementary recommendation methodology, focusing on book recommendation. By retrieving web reviews of books using existing Web Services, an infrastructure has been developed for need-based book recommendation system. Implementation results shows that our book recommendation allows a user to eliminate irrelevant books and presents the desired books to the user from given book set. The proposed book recommender is one of the first systems in terms of focusing on meeting individuals’ needs rather than calculating similarity or preferences automatically, which is adopted by the traditional recommender system.

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References

  1. Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Recommender System Based on Consumer Product Reviews. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 719–723 (2006)

    Google Scholar 

  2. Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed recommender: basing recommendations on consumer product reviews. IEEE Intelligent Systems 22(3), 39–47 (2007)

    Article  Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  4. Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  5. Jin, R., Si, L., Zhai, C.: Preference-based Graphic Models for Collaborative Filtering. In: Proceedings of Nineteenth Conference on Uncertainty in Artificial Intelligence (August 2003)

    Google Scholar 

  6. Jin, R., Si, L., Zhai, C., Callan, J.: Collaborative Filtering with Decoupled Models for Preferences and Ratings. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 309–316 (November 2003)

    Google Scholar 

  7. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)

    Article  Google Scholar 

  8. Kuroiwa, T., Bhalla, S.: Dynamic Personalization for Book Recommendation System using Web Services and Virtual Library Enhancements. In: CIT 2007. IEEE 7th International Conference on Computer and Information Technology, University of Aizu, Japan (2007)

    Google Scholar 

  9. Lang, K.: NewsWeeder: Learning to Filter Netnews. In: Proceedings of the 12th International Conference on Machine Learning, pp. 331–339 (1995)

    Google Scholar 

  10. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 76–80 (January/February 2003)

    Google Scholar 

  11. Mooney, R.J., Roy, L.: Content-based Book Recommending Using Learning for Text Categorization. In: Proceedings of the 5th ACM conference on Digital Libraries, pp. 195–204. ACM Press, New York (2000)

    Chapter  Google Scholar 

  12. Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27, 313–331 (1997)

    Article  Google Scholar 

  13. Pazzani, M.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 393–408 (December 1999)

    Google Scholar 

  14. Amazon Web Service. http://aws.amazon.com/

  15. Technorati API. http://technorati.com/developers/api/

  16. Xpriori (XMS), http://www.xpriori.com/

  17. Yahoo! Search Web Service, http://developer.yahoo.com/search/

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Subhash Bhalla

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© 2007 Springer-Verlag Berlin Heidelberg

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Kuroiwa, T., Bhalla, S. (2007). Aizu-BUS: Need-Based Book Recommendation Using Web Reviews and Web Services. In: Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2007. Lecture Notes in Computer Science, vol 4777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75512-8_21

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  • DOI: https://doi.org/10.1007/978-3-540-75512-8_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75511-1

  • Online ISBN: 978-3-540-75512-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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