Abstract
In web recommender systems, clustering is done offline to extract usage patterns and a successful recommendation highly depends on the quality of this clustering solution. As for collaborative recommendation, there are two ways to calculate the similarity for clique recommendation: Item-based Clustering Method and User-based Clustering Method. Researches have proved that item-based collaborative filtering is better than user-based collaborative filtering at precision and computation complexity. However, the common item-based clustering technologies could not quite suit for news recommender system, since the news events evolve fast and continuous. In this paper, we suggest using technologies of TDT to group news items instead of common item-based clustering technologies. Experimental results are examined that shows the usefulness of our approach.
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References
Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM Press, Melbourne (1998)
Demir, G.N., Uyar, A.S., Oguducu, S.G.: Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems. In: 9th annual conference on Genetic and evolutionary computation, London, pp. 1943–1950 (2007)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. In: Arnold, E. (ed.), London, UK (2001)
Jin, H., Schwartz, R., Sista, S., Walls, F.: Topic tracking for radio, TV broadcast, and newswire. In: Proceedings of the DARPA Broadcast News Workshop, Herndon, pp. 199–204 (1999)
Juha, M., Helena, A.-M., Mako, S.: Simple semantics in topic detection and tracking. J. Information Retrieval. 7(3-4), 347–386 (2004)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley Sons, New York (1990)
Lee, C., Lee, G.G., Jang, M.: Dependency structure language model for topic detection and tracking. J. Information Processing and Management 43(5), 1249–1259 (2007)
Li, B.L., Li, W.J., Lu, Q.: Enhancing topic tracking with temporal information. In: 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, pp. 667–668 (2006)
Martin, A., Doddington, G., Kamm, T., Ordowski, M., Prazybocki, M.: The DET curve in assessment of detection task performance. In: Euro Speech, pp. 1895–1898 (1997)
Miller, D.R.H., Leek, T., Schwartz, R.M.: A hidden markov model information retrieval system. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information retrieval, Berkeley, pp. 214–221 (1999)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Tenth Int. WWW Conf., pp. 285–295. ACM Press, Hongkang (2001)
Spitters, M., Kraaij, W.: A language modeling approach to tracking news events. In: Proceedings of TDT Workshop 2000, Gaithersburg, pp. 101–106 (2000)
TDT 2000. The year 2000 topic detection and tracking (TDT 2000) task definition and evaluation plan, Version 1.4 (2000)
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Qiu, J., Liao, L., Li, P. (2009). News Recommender System Based on Topic Detection and Tracking. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_87
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DOI: https://doi.org/10.1007/978-3-642-02962-2_87
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