Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Searching for Similar Informational Articles in the Internet Channel

Authors: Sung Ho Ha, Seong Hyeon Joo, Hyun U. Pae


In terms of total online audience, newspapers are the most successful form of online content to date. The online audience for newspapers continues to demand higher-quality services, including personalized news services. News providers should be able to offer suitable users appropriate content. In this paper, a news article recommender system is suggested based on a user-s preference when he or she visits an Internet news site and reads the published articles. This system helps raise the user-s satisfaction, increase customer loyalty toward the content provider.

Keywords: Content classification, content recommendation, customer profiling, documents clustering.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1515


[1] C. Ihlstrom and J. Palmer, "Revenues for online newspapers: owner and user perceptions," Electronic Markets, vol. 12, no. 4, pp. 228-236, 2002.
[2] R. Shah, R. Jain, and F. Anjum, "Efficient dissemination of personalized information using content-based multicast," Proc. Of IEEE-Infocom, 2002, Jun. 23-27.
[3] V. K. Gupta, S. Govindarajan, and T. Johnson, "Overview of content management approaches and strategies," Electronic Markets, vol. 11, no. 4, pp. 281-288, 2001.
[4] S. Kienle, S. Lingler, W. Kraas, A. Offenhausser, W. Knol, G. Jung, A. L. K. Wee, C. T. Loong, and J. C. Tiak, "DeNews - a personalized news system," Expert Systems with Applications, vol. 13, no. 4, pp. 249-257, 1997.
[5] K. Bharat, T. Kamba, and M. Albers, "Personalized, interactive news on the Web," Multimedia Systems, vol. 6, pp. 349-358, 1998.
[6] D. Konstantas and J.-H. Morin, "Agent-based commercial dissemination of electronic information," Computer Networks, vol. 32, pp. 753-765, 2000.
[7] S. Jokela, M. Turpeinen, T. Kurki, E. Savia, and R. Sulonen, "The role of structured content in a personalized news service,", Proc. of the 34th Hawaii International Conference on System Sciences, 2001, Jan. 3-6, pp. 1-10.
[8] A. Kohrs and B. Merialdo, "Creating user-adapted Websites by the use of collaborative filtering," Interacting with Computers, vol. 13, pp. 695-716, 2001.
[9] F.-F. Kuo and M.-K. Shan, "A personalized music filtering system based on melody style classification," Proc. of the 2002 IEEE International Conference on Data Mining, 2002, pp. 649-652.
[10] W. Shi, E. Collins, and V. Karamcheti, "Modeling object characteristics of dynamic Web content," Journal of Parallel and Distributed Computing, vol. 63, pp. 963-980, 2003.
[11] D. Zhang, "Delivery of personalized and adaptive content to mobile devices: a framework and enabling technology," Communications of the Association for Information Systems, vol. 12, pp. 183-202, 2003.
[12] B. L. Tseng, C.-Y. Lin, and J. R. Smith, "Video personalization and summarization system for usage environment," Journal of Visual Communication & Image Representation, vol. 15, pp. 370-392, 2004.
[13] B. L. D. Bezerra and F. A. T. Carvalho, "A symbolic approach for content-based information filtering," Information Processing Letters, 92, pp. 45-52, 2004.
[14] M. Boavida, S. Cabaco, and N. Correia, "VideoZapper: a system for delivering personalized video content," Multimedia Tools and Applications, vol. 25, pp. 345-360, 2005.
[15] R.-L. Liu and W.-J. Lin, "Incremental mining of information interest for personalized web scanning," Information Systems, vol. 30, pp. 630-648, 2005.
[16] Y. Li, L. Lu, and L. Xuefeng, "A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-commerce," Expert Systems with Applications, vol. 28, pp. 67-77, 2005.
[17] C.-P. Wei, R. H. L. Chiang, and C.-C. Wu, "Accommodating individual preferences in the categorization of documents: a personalized clustering approach," Journal of Management Information Systems, vol. 23, no. 2, pp. 173-201, 2006.
[18] Q. Li, S. H. Myaeng, and B. M. Kim, "A probabilistic music recommender considering user opinions and audio features," Information Processing and Management, vol. 43, pp. 473-487, 2007.
[19] P. Kazienko and M. Adamski, "AdROSA - Adaptive personalization of web advertising," Information Sciences, vol. 177, pp. 2269-2295, 2007.
[20] M.-H. Hsu, "A personalized English learning recommender system for ESL students," Expert Systems with Applications, vol. 34, pp. 683-688, 2008.
[21] M.-F. Moens, Automatic indexing and abstracting of document texts, MA: Kluwer Academic Publishers, 2000.
[22] G. Kowalski and M. T. Maybury, Information storage and retrieval systems: theory and implementation, MA: Kluwer Academic Publishers, 2000.
[23] S. M. Weiss, N. Indurkhya, T. Zhang, and F. J. Damerau, Text mining: predictive methods for analyzing unstructured information, NY: Springer, 2007.
[24] M. Konchady, Text mining application programming, Charles River Media, 2006.
[25] M. Mohammadian, Intelligent agents for data mining and information retrieval, PA:Idea Group Publishing, 2004.