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Evaluation of Models for Semantic Information Filtering

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 179))

Abstract

In this paper we evaluate various approaches to a user profile modelling for news recommendation. We represent a user profile as a bag of real world entities, the user is interested in. News articles are thus recommended based on its contained concepts and not based on a text similarity. We propose several ways of such a user profile construction based on a user feedback. Different ways of a user feedback collection are compared. This paper addresses the problem of precise user modelling for information filtering.

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Notes

  1. 1.

    http://news.google.com/

References

  1. Lašek, I., Vojtáš, P.: Semantic information filtering—beyond collaborative filtering. In: 4th International Semantic Search Workshop. http://km.aifb.kit.edu/ws/semsearch11/11.pdf (2011). Accessed 13 June 2011

  2. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  3. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Cooper, G.F., Moral, S. (eds.) Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 43–52. Morgan-Kaufmann, San Francisco, Calif (1998)

    Google Scholar 

  4. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl J.: GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the: ACM Conference on Computer supported Cooperative Work (CSCW ’94), pp. 175–186. ACM, New York, NY, USA (1994)

    Google Scholar 

  5. Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web (WWW ’07), pp. 271–280. ACM, New York, NY, USA (2007)

    Google Scholar 

  6. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: IUI’10: Proceedings of the: International Conference on Intelligent User. Interfaces, pp. 31–40 (2010)

    Google Scholar 

  7. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  8. Robertson, S., Walker, S.: Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In: Proceedings of SIGIR ’94, pp. 232–241. ACM Press, New York, NY (1994)

    Google Scholar 

  9. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of SIGIR ’99, pp. 275–281. ACM Press, New York, NY (1998)

    Google Scholar 

  10. Bogers, T., Bosch, A.: Comparing and evaluating information retrieval algorithms for news recommendation. In: Proceedings of the ACM Conference on Recommender Systems. pp. 141–144. (2007)

    Google Scholar 

  11. Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X., Chang, Y.: Learning to model relatedness for news recommendation. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 57–66. ACM Press (2011)

    Google Scholar 

  12. Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J. A., Zien, J.Y.: Semtag and seeker: bootstrapping the semantic web via automated semantic annotation. In: Proceedings of the 12th International Conference on World Wide Web, WWW’03, pp. 178–186. ACM, New York, NY, USA, 2003

    Google Scholar 

  13. Popov, B., Kiryakov, A., Ognyanoff, D., Manov, D., Kirilov, A.: Kim—a semantic platform for information extraction and retrieval. Nat. Lang. Eng. 10, 375–392 (2004)

    Article  Google Scholar 

  14. Chang, C.-H., Kayed, M., Girgis, M.R., Shaalan, K.: A survey of web information extraction systems. IEEE Trans. Knowl. Data Eng. 18, 1411–1428 (2006)

    Article  Google Scholar 

  15. Reeve, L., Han, H.: Survey of semantic annotation platforms. In: Proceedings of the 2005 ACM Symposium on Applied Computing, SAC ’05, pp. 1634–1638. ACM, New York, NY, USA (2005)

    Google Scholar 

  16. Dotsika, F.: Semantic apis: scaling up towards the semantic web. Int. J. Inf. Manag. 30(4), 335–342 (2010)

    Article  Google Scholar 

  17. Dapper. http://www.dapper.net (2005). Accessed 13 June 2011

  18. Zemanta. http://www.zemanta.com/api/ (2009). Accessed 13 June 2011

  19. OpenCalais. http://www.opencalais.com/ (2008). Accessed 13 June 2011

  20. TAP. http://ksl.stanford.edu/projects/TAP/ (2005). Accessed 13 June 2011

  21. Jena. http://jena.sourceforge.net/. Accessed 13 June 2011

  22. Lewis, D., Yang, Y., Rose, T., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. (JMLR) 5, 361–397 (2004)

    Google Scholar 

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Acknowledgments

This work has been partially supported by the grant of The Czech Science Foundation (GAČR) P202/10/0761 and by the grant of Czech Technical University in Prague registration number SGS11/085/OHK3/1T/18.

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Correspondence to Ivo Lašek .

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Lašek, I., Vojtáš, P. (2013). Evaluation of Models for Semantic Information Filtering. In: Kudělka, M., Pokorný, J., Snášel, V., Abraham, A. (eds) Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011. Advances in Intelligent Systems and Computing, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31603-6_19

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  • DOI: https://doi.org/10.1007/978-3-642-31603-6_19

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