new event detection for web page reccomendation using web mining
Dhaslima Nasrin.S,Mubina.A,Shanmugapriya .K
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 27 March,2017 Pages:1245-1252
Abstract
In this paper, we propose an uncertainty analysis of the online events and its application to Webpage recommendations by a new approach to observe, summarize and track events from a collection of new Web pages. Given a set of web services, we calculate response-time for the web services and also obtain feedback to improve the web service by the means of HMM model. We examine some experimental results and show the usefulness of our approach. The literature of Webpage recommendations will be roughly classified into two categories: 1) non content based methods and 2) content-based strategies. We tend to propose a framework to identify the various underlying levels of linguistics uncertainty in terms of internet events, and then utilize these for Webpage recommendations. Our plan is to contemplate an internet event as a system composed of various keywords, and therefore the uncertainty of this keyword system is expounded to the uncertainty of the actual Web event, we tend to establish the different levels of linguistics uncertainty, and construct a linguistics pyramid to precise the uncertainty hierarchy of an internet event.
Kewords
Social event, uncertainty analysis, Web event, Web mining, Web page recommendation
Reference
[1] Junyu Xuan , “Uncertainity Analysis for the Keyword System of Web Events,” IEEE Transactions On Systems, Man, And Cybernetics: Systems, Vol. 46, No. 6, June 2016 [2] F.Can et al.,”New event detection and topic tracking in Turkish,” J.Amer.Soc.inf.Sci.Technol.,vol.61,no. 4,pp. 802-819, Apr. 2010 [3] G.Kumaran and J.Allan ,”Text classification and named entities for new event detection” in Proc .16th ACM SIGKDD int. conf. Res new Develop .Inf. Reterival sheffield, UK.,2004,pp.297-304 [4] J.Leskovec,l.Backstrom, and J.Kleinberg ,Meme-tracking and the dynamics of the news cycle,”in Proc.15th ACM SIGKDD Int.Conf.Knowl .Disc.Data Min [5] T. Brants, F. Chen, and A. Farahat, “A system for new event detection,” in Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, Toronto, ON, Canada, 2003, pp. 330–337. [6] M. Gomez Rodriguez, J. Leskovec, and A. Krause, “Inferring networks of diffusion and influence,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Washington, DC, USA, 2010, pp. 1019–1028 [7] T. Iwata, T. Yamada, Y. Sakurai, and N. Ueda, “Sequential modeling of topic dynamics with multiple timescales,” ACM Trans. Knowl. Disc. Data, vol. 5, no. 4, Feb. 2012, Art. ID 19. [8] Q. Mei and C. Zhai, “Discovering evolutionary theme patterns from text: An exploration of temporal text mining,” in Proc. 11th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Chicago, IL, USA, 2005, pp. 198–207. [9] S. Morinaga and K. Yamanishi, “Tracking dynamics of topic trends using a finite mixture model,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., Seattle, WA, USA, 2004, pp. 811–816. [10] M. G. Morgan and M. Small, Uncertainty: A Guide to Dealing With Uncertainty in Quantitative Risk and Policy Analysis. New York, NY, USA: Cambridge Univ. Press, 1992. [11] K. Hayes, “Uncertainty and uncertainty analysis methods,” Aust. Centre Excellence Risk Assess. (ACERA) Project A 705, Tech Rep. EP102467, 2011
