To Assess Numerous Procedures in combination with a Neural learning plan to semantically classify short texts

Vijya Sai KumarSheela, A. Ramesh, K. Ramesh

Abstract


In OSNs, information filtering can also be used for a unlike, more aware, principle. This is appropriate to the statement that in OSNs there is the leeway of redistribution or mentions other posts on fastidious public/private areas, called in general walls. Information filtering can as a result be used to give users the facility to repeatedly control the messages written on their own walls, by filtering out unwanted messages. We deem that this is a key OSN service that has not been present so far. We suggest a scheme agree to OSN users to have a straight control on the messages position on their walls. This is attain through a supple rule-based system, that allows users to modify the filtering decisive factor to be practical to their walls, and a Machine Learning-based soft classifier automatically labelling messages in hold up of content-based filtering.


References


A. Adomavicius and G. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.

M. Chau and H. Chen, “A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis,” Decision Support Systems, vol. 44, no. 2, pp. 482-494, 2008.

R.J. Mooney and L. Roy, “Content-Based Book Recommending Using Learning for Text Categorization,” Proc. Fifth ACM Conf. Digital Libraries, pp. 195-204, 2000.

F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002. [5] M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari,

“Content-Based Filtering in On-Line Social Networks,” Proc. ECML/PKDD Workshop Privacy and Security Issues in Data Mining and Machine Learning (PSDML ’10), 2010.

N.J. Belkin and W.B. Croft, “Information Filtering and Information Retrieval: Two Sides of the Same Coin?” Comm. ACM, vol. 35, no. 12, pp. 29-38, 1992.

P.J. Denning, “Electronic Junk,” Comm. ACM, vol. 25, no. 3, pp. 163-165, 1982.

P.W. Foltz and S.T. Dumais, “Personalized Information Delivery: An Analysis of Information Filtering Methods,” Comm. ACM,vol. 35, no. 12, pp. 51-60, 1992.

P.S. Jacobs and L.F. Rau, “Scisor: Extracting Information from On- Line News,” Comm. ACM, vol. 33, no. 11, pp. 88-97, 1990. [10] S. Pollock, “A Rule-Based Message Filtering System,” ACM Trans. Office Information Systems, vol. 6, no. 3, pp. 232-254, 1988.

P.E. Baclace, “Competitive Agents for Information Filtering,” Comm. ACM, vol. 35, no. 12, p. 50, 1992.

P.J. Hayes, P.M. Andersen, I.B. Nirenburg, and L.M. Schmandt, “Tcs: A Shell for Content-Based Text Categorization,” Proc. Sixth IEEE Conf. Artificial Intelligence Applications (CAIA ’90), pp. 320- 326, 1990.

G. Amati and F. Crestani, “Probabilistic Learning for Selective Dissemination of Information,” Information Processing and Management, vol. 35, no. 5, pp. 633-654, 1999.

M.J. Pazzani and D. Billsus, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Machine Learning, vol. 27, no. 3, pp. 313-331, 1997.

Y. Zhang and J. Callan, “Maximum Likelihood Estimation for Filtering Thresholds,” Proc. 24th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 294-302, 2001.


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