Research of Web Data Mining in Personalized Recommendation Service

Ge Wang; Chan Juan Liu; Peng Bo Pu

doi:10.4028/www.scientific.net/AMR.546-547.429

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 546-547Research of Web Data Mining in Personalized...

Research of Web Data Mining in Personalized Recommendation Service

Abstract:

To offer personalized recommendation service to web users, it adopts improved FP-Growth algorithm, introduces its implementation methods and directly applies it to the recessive knowledge mining of web site information category in details. Via mining the website data and analysing association rules, useful relavant knowledge is obtained, tendancy of website visiting can be prediced and personalized service will be prescribed which make website more friendly and satisfactory.

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Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

429-434

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.546-547.429

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Online since:

July 2012

Authors:

Ge Wang, Chan Juan Liu, Peng Bo Pu

Keywords:

Association Rule, Data Mining (DM), FP-Growth Algorithm, Personalized Recommendation

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References

[1] Evfimievski A, Srikant R and Agrawal R, Privacy preserving mining of association rules, in Information Systems, vol. 29, pp.343-364, (2004).

DOI: 10.1016/j.is.2003.09.001

Google Scholar

[2] J Han, J Pei and Y Yin, Mining frequent patterns without candidate generation, in Proc ACM SIGMOD, 2000, p.1~12.

DOI: 10.1145/335191.335372

Google Scholar

[3] Yebai Li, Hui Tang, Chun Zhang and Yuming He, Frequent pattern mining algorithm based on improved FP-tree, in Journal of Computer Applications, vol. 31, No. 1, pp.101-103, (2011).

DOI: 10.3724/sp.j.1087.2011.00101

Google Scholar

[4] Grahne G and Jianfei Zhu, Fast algorithm for frequent item set mining using FP-trees, in IEEE Transactions on Knowledge and Data Engineering, vol. 17, No. 10, pp.1347-1362 , (2005).

DOI: 10.1109/tkde.2005.166

Google Scholar

[5] Agrawal R, Imielienski T, Swami A, Mining Association Rules between Sets of Items in large Databases, in Proceeding of the ACM SIGMOD international Conference on Management of Data, vol. 2, pp.207-216, (1993).

DOI: 10.1145/170036.170072

Google Scholar

[6] Xian-feng Liu and Ge Zeng, Model based on semi-structured data for association rules mining, in Computer Engineering and Applications, vol. 43, No. 19, pp.171-173, (2007).

Google Scholar