Analyzing the Relation of Community Group for Research Paper Bookmarking by Using Association Rule
Authors: P. Jomsri
Abstract:
Currently searching through internet is very popular especially in a field of academic. A huge of educational information such as research papers are overload for user. So community-base web sites have been developed to help user search information more easily from process of customizing a web site to need each specifies user or set of user. In this paper propose to use association rule analyze the community group on research paper bookmarking. A set of design goals for community group frameworks is developed and discussed. Additionally Researcher analyzes the initial relation by using association rule discovery between the antecedent and the consequent of a rule in the groups of user for generate the idea to improve ranking search result and development recommender system.
Keywords: association rule, information retrieval, research paper bookmarking.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329192
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1443References:
[1] CiteULike, http://www.CiteULike.org
[2] Connotea, http://www.connotea.org
[3] BibSonomy, http://www.bibsonomy.org
[4] C. Senot, D. Kostadinov, M. Bouzid, J. Picault, A. Aghasaryan, and C. Bernier ,"Analysis of Strategies for Building Group Profiles,"in User Modeling, Adaptation, and Personalization 2010, Lecture Notes in Computer Science, 2010, Volume 6075/2010, pp 40-51.
[5] P. jomsri, A Combination of Similarity Ranking and Time for Social Research Paper Searching, World Academy of Science, Engineering and Technology 78 2011,pp. 638-643
[6] P.Jomsri, S. Sanguansintukul, W. Choochaiwattana, "Improve Research paper Searching with social tagging-A Preliminary Investigation," in the Eight International Symposium on Natural Language Processing, Thailand, 2009,pp.152-156.
[7] P.Jomsri, S. Sanguansintukul, W. Choochaiwattana, "A Comparison of Search Engine Using "Tag Title and Abstract' with CiteULike — An Initial Evaluation," in the 4th IEEE Int. Conf for Internet Technology and Secured Transactions (ICITST-2009),United Kingdom,2009.
[8] Capocci, and G.Caldarelli, "Folksonomies and Clustering in the Collaborative System CiteULike," arXiv Press, eprint No. 0710.2835, 2007.
[9] U. Farooq, T.G. Kannampallil, Y. Song, C.H. Ganoe, M.C., John, L. Giles, "Evalating Tagging Behavior in Social Bookmarking Systems: Metrics and design heuristics," in Proc. of the 2007 international ACM conference on Supporting group work (GROUP '07), Sanibel Island, Florida, USA, 2007,pp.351-360.
[10] T. Bogers, and A. van den Bosch, "Recommending Scientific Articles Using CiteULike," in Proc. of the 2008 ACM conference on Recommender systems (RecSys '08), Switzerland,2008 ,pp.287-290.
[11] E. Santos-Neto, M. Ripeanu, and A. Iamnitchi, " Tracking usage in collaborative tagging communities".
[12] P. Heymann, D. Ramage, and H. Garcia-Molina, "Social tag prediction," in SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM, 2008, pp.
[13] C. Schmitz, A. Hotho, R. rlschke, and G. S. and,"Mining association rules in folksonomies," in DataScience and Classification, ser. Studies in Classification,Data Analysis, and Knowledge Organization. SpringerBerlin Heidelberg, 2006, pp. 261-270.
[Online].Available:http://www.springerlink.com/content/gmv832553g0x 3673/
[14] U. Farooq, C.H. Ganoe, J.M. Carroll, and C.L. Giles, "Supporting distributed scientific collaboration: Implications fordesigning the CiteSeer collaborator," in IEEE Proc. of the Hawaii Int'l Conference on System Sciences, Waikoloa, Hawaii,2007.
[15] R. Cohen, and S. Havlin, "Scale-free network are ultrasmall Physica", A311, p590
[16] Peng Zhang, Zengru Di, Complex System and Complexity Science, 2(3), pp.30-34
[17] W. Hong, W. Wei-dong , X. Na, H. Wen, "Group Personnel Relationship Analysis Based on Social Networks", in IEEE International Symposium on IT in Medicine & Education, 2009. (ITTME '09),pp.1003 — 1008
[18] X. Chen, and Y. Wu. Personalized Knowledge Discovery: Mining Novel Association Rules from Tex t. Available: http://www.siam.org/meetings/sdm06/proceedings/067chenx.pdf
[19] C. Schmitz, A. Hotho, R. Jaschke, and G. Stumme. (2008, Oct). Mining Association rule in Folksonomies. Journal of Information Science (JIS)
[Online] .Available:http://citeseerx.ist.psu.edu/viewdoc/download?doi= 10. 1. 1.93 .9741 &rep =rep 1 &type=pdf.
[20] C. Haruechaiyasak, M. Shyu, and S. Chen, "A Data mining Framework for Building A Web-Page Recommender System", Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, IRI - 2004, November 8-10, 2004, Las Vegas Hilton, Las Vegas, NV, USA. pp. 357-362
[21] R. Forsati, M.R. Meybodi, A. Ghari Neiat, "Web Page Personalization based on Weighted Association Rules", International Conference on Electronic Computer technology 2009, pp. 130-135
[22] S. niwa , T. Doi, and S. Honiden, " Web Page Recommender System based on Folksonomy Mining for ITNG'06 Submissions", Proceedings of the Third International Conference on Information Technology:New Generations (ITNG'06)
[23] Hui Chang, Daren He, Science and Technology Review , 24(9),pp. 84-87