Abstract
With rapid development of Internet, information and resource of Web academic database is great and explosive growth, so it is difficult to quickly and accurately obtain information which meets individual user’s needs. Web personalized services can effectively solve the problem of information overload problem and alleviate user’s cognitive burden. How to predict user interest is a key issue in Web personalized services. First, this paper proposes concepts of user knowledge unit and user knowledge flow that represents user short-term interest and long-term interest respectively. Second, existing methods have some defects which can’t be sensitive to perceive user interest change and accurately predict user real-time interest; we put forward Collaborative Time Weight (CTW) and Collaborative Relation Weight (CRW) to solve those problems. Meanwhile the prediction algorithm for user real-time interest is proposed based on collaborative filtering and interactive computing. Finally, experimental results demonstrate that our method can capture user real-time interests accurately and alleviate the user’s cognitive burden effectively.
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References
Guang, Y.K., Zhou, M.: Resume information extraction with cascaded hybrid model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL), pp. 499–506 (2005)
Tang, J., Zhang, D., Yao, L.: A Combination approach to web user profiling. Knowledge Discovery from Data 5(1), Article 2 (2010)
Jung, S.Y., Hong, J.H., Kim, T.S.: A Statistical Model for User Preference. Knowledge and Data Engineering 17(6) (2005)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web Search Based on User Profile Constructed without Any Effort from Users, May 17–22. ACM, New York (2004), 1-58113-844-X/04/0005
Zhang, Z.K., Zhou, T., Zhang, Y.C.: Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs. Physica A 389, 179–186 (2010)
Nkenberg, K.L.: Learning drifting concep: example selection vs example weighting. Intelligent Data Analysis 8(3), 281–300 (2004)
Oychev, K., Schwab, I.: Adaptation to drifting user’s intersects. In: Proceedings of ECML 392 45 (2000)
Xu, Y.: The dynamics of interactive information retrieval behavior part i: An activity theory perspective. Journal of the American Society for Information Science and Technology 58(7), 958–970 (2007)
Garcia, P., Amandi, A., Schiaffino, S., Campo, M.: Evaluating Bayesian Networks’ Precision for Detecting Students’ Learning Styles. Computers and Education 49(3), 794–808 (2007)
Annibelli, V., Godoy, D., Amandi, A.: A Genetic Algorithm Approach to Recognize Students’ Learning Styles. Interactive Learning Environments 14(1), 55–78 (2006)
Piwowarski, B., Lalmas, M.: A Quantum-based Model for Interactive Information Retrieval (extended version). ArXiv e-prints (0906.4026) (2009)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithm for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Nissen, M.E.: An Extended Model of Knowledge Flow Dynamics. Communications of the Association for Information Systems, 251–266 (2002)
Yu, J., Liu, F.F., Gong, J.: Discovering Collaborative Users based on Query Context for Web Information Seeking. In: Proceedings of the 2th International Conference on Future Computer and Communication (2010)
Yu, J., Liu, F.F., Zhao, H.H.: Building User Profile based on Concept and Relation for Web Personalized Services. In: International Conference on Innovation and Information Management (2012)
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Yu, J., Zhao, H., Liu, F. (2012). User Real-Time Interest Prediction Based on Collaborative Filtering and Interactive Computing for Academic Recommendation. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_16
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DOI: https://doi.org/10.1007/978-3-642-31576-3_16
Publisher Name: Springer, Berlin, Heidelberg
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