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2016 | 40 | 163-174
Article title

Survey on Message Filtering Techniques for On-line Social Network

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EN
Abstracts
EN
A social network is a set of people or organizations or other social entities connected by set of social relationships such as friendship, co-working or information exchange. Online Social Networks (OSN) usually not support to the user for message filtering. To solve this issue, which allows OSN users to have a direct control on the messages posted on their walls. The users can control the unwanted messages posted on their own private space .To avoid unwanted messages displayed and they can also block their friend from friends list using filtering rule, content based filtering and short text classification.
Year
Volume
40
Pages
163-174
Physical description
References
  • [1] R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195-204.
  • [2] R. Prashant Tomer, “On Line Social Network Content and Image Filtering Classifications,” ISSN 2319-5991 Vol. 2, No. 4, © 2013 IJERST. November 2013.
  • [3] M. Carullo, E. Binaghi, I. Gallo, and N. Lamberti, “Clustering of short commercial documents for the web,” in Proceedings of 19th International Conference on Pattern Recognition (ICPR 2008), 2008.
  • [4] F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys, Vol. 34, no. 1, pp. 1-47, 2002.
  • [5] A. Adomavicius, G.and Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-theart and possible extensions,” IEEE Transaction on Knowledge and Data Engineering, Vol. 17, no. 6, pp. 734-749, 2005.
  • [6] D. D. Lewis, Y. Yang, T. G. Rose, and F. Li, “Rcv1: A new benchmark collection for text categorization research,” Journal of Machine Learning Research, 2004.
  • [7] Bonchi and E. Ferrari, Privacy-aware Knowledge Discovery: Novel Applications and New Techniques. Chapman and Hall/CRC Press, 2010.
  • [8] B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, and M. Demirbas, Short text classification in twitter to improve information filtering, in Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, 2010, pp. 841-842.
  • [9] DIK L. LEE "Document Ranking and the Vector-Space Model" Hong Kong University of Science and Technology HUEI CHUANG, Information Dimensions KENT SEAMONS, Transarc.
  • [10] Carla Teixeira Lopes "Context Features and their use in Information Retrieval" Doctoral Program in Informatics Engineering of Faculdade de Engenharia da Universidade, 2007.
  • [11] N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: Two sides of the same coin?” Communications of the ACM, Vol. 35, no. 12, pp. 29-38, 1992.
  • [12] S. Dumais, J. Platt, D. Heckerman, and M. Sahami, “Inductive learning algorithms and representations for text categorization,” in Proceedings of Seventh International Conference on Information and Knowledge Management (CIKM98), 1998, pp. 148-155.
  • [13] D. D. Lewis, “An evaluation of phrasal and clustered representations on a text categorization task,” in Proceedings of 15th ACM International Conference on Research and Development in Information Retrieval (SIGIR-92), N. J.
  • [14] Belkin, P. Ingwersen, and A. M. Pejtersen, Eds. ACM Press, New York, US, 1992, pp. 37-50.
  • [15] R. E. Schapire and Y. Singer, “Boostexter: a boosting-based system for text categorization,” Machine Learning, Vol.39, no. 2/3, pp. 135-168, 2000.
  • [16] E. D. Wiener, J. O. Pedersen, and A. S. Weigend, “A neural network approach to topic spotting,” in Proceedings of the Annual Symposium on Document Analysis and Information Retrieval (SDAIR-95), Las Vegas, US, 1995, pp. 317-332.
  • [17] S. Zelikovitz and H. Hirsh, “Improving short text classification using unlabeled background knowledge,” in Proceedings of 17th International Conference on Machine Learning (ICML-00), P. Langley, Ed. Stanford, US: Morgan Kaufmann Publishers, San Francisco, US, 2000, pp. 1183-1190.
  • [18] www.en.m.wikipedia.org.
  • [19] T. M. Mitchell. Machine Learning. WCB/McGraw-Hill, 1997.
  • [20] Y. Li, A. Jain. Classification of text documents. The Computer Journal, 41(8), pp. 537-546, 1998.
Document Type
article
Publication order reference
YADDA identifier
bwmeta1.element.psjd-e08522dc-8187-44b8-af72-7ab27bf4ba33
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