Abstract
In many document retrieval systems the user is not supported until sufficient information about him is collected. In some other systems randomly selected documents are recommended but they may not be relevant. To avoid so-called “cold-start problem” a method for determining a non-empty profile for a new user is presented in this paper. The experimental evaluations are usually performed using a few real users. This is a time- and cost-consuming method of evaluations, so we propose the methodology of experiments using simulations of user activities. The results were statistically analyzed and have shown that using the proposed method, the adaptation process allows to building a profile that is closer to user preference than in the situation when the first user profile is empty.
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References
Galassi, U., Giordana, A., Saitta, L., Botta, M.: Learning Profiles Based on Hierarchical Hidden Markov Model. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 47–55. Springer, Heidelberg (2005)
Indyka-Piasecka, A.: Using Multi-attribute Structures and Significance Term Evaluation for User Profile Adaptation. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 336–345. Springer, Heidelberg (2011)
Ingwersen, P.: The User in Interactive Information Retrieval Evaluation. In: Melucci, M., Baeza-Yates, R. (eds.) Advanced Topics in Information Retrieval. The Information Retrieval Series, vol. 33. Springer, Heidelberg (2011)
Jung, S., Herlocker, J.L., Webster, J.: Click data as implicit relevance feedback in web search. Information Processing and Management 43, 791–807 (2007)
Kagolovsky, Y., Moehr, J.R.: Current Status of the Evaluation of Information Retrieval. Journal of Medical Systems 27(5), 409–424 (2003)
Kiewra, M.: Hybrid method for document recommendation in hypertext environment. PhD dissertation. Wroclaw University of Technology (2006)
Li, S., Wu, G., Hy, X.: Hierarchical User Interest Modeling for Chinese Web Pages. In: Proceedings of International Conference on Internet Multimedia Computing and Service (ICIMCS 2011), pp. 164–169 (2011)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2009)
Mianowska, B., Nguyen, N.T.: A Method for Collaborative Recommendation in Document Retrieval Systems. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part II. LNCS (LNAI), vol. 7803, pp. 168–177. Springer, Heidelberg (2013)
Mianowska, B., Nguyen, N.T.: Tuning User Profiles Based on Analyzing Dynamic Preference in Document Retrieval Systems. Multimedia Tools and Applications 65(1), 93–118 (2013)
Montaner, M., Lopez, B., Rosa, J.: A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review 19, 285–330 (2003)
Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer (2008)
Ren, F., Bracewell, D.B.: Advanced Information Retrieval. Electronic Notes in Theoretical Computer Science 225, 303–317 (2009)
Schein, A.I., Popescu, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for ColdStart Recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), pp. 253–260 (2002)
Wang, Y.D., Forgionne, G.: Testing a Decision-Theoretic Approach to the Evaluation of Information Retrieval Systems. Journal of Information Science 34(6), 861–876 (2008)
Wolfe, S.R., Zhang, Y.: Interaction and Personalization of Criteria in Recommender Systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 183–194. Springer, Heidelberg (2010)
Hong, T.P., Liou, Y.L., Wang, S.L., Vo, B.: Feature selection and replacement by clustering attributes. Vietnam Journal of Computer Science (November 2013), doi:10.1007/s40595-013-0004-3
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Maleszka, B., Nguyen, N.T. (2014). Evaluating Profile Convergence in Document Retrieval Systems. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_17
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DOI: https://doi.org/10.1007/978-3-319-05476-6_17
Publisher Name: Springer, Cham
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