Searching for Similar Informational Articles in the Internet Channel
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:61726", author = "Sung Ho Ha and Seong Hyeon Joo and Hyun U. Pae", title = "Searching for Similar Informational Articles in the Internet Channel", abstract = "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.", volume = "1", number = "11", pages = "3662-4", }
