Mining web access log for the personalization recommendation

Xueping Peng*, Yujuan Cao, Zhendong Niu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

This paper presents a personalization recommenddation model to recommend potentially interesting resources to users based on the web access log of users. This model builds on the apriori algorithm and the tf-idf technology, which consists of three parts: resource description, user's preference extraction and the personalization recommendation. Firstly ,our model generates resource text space vector by analyzing the resource information achieved by mining user's web access log, then it attains interest set to make use of the apriori algorithm based on the vector, finally, it recommends filtered and sorted resources to users content based recommendation model.

Original languageEnglish
Title of host publicationProceedings - 2008 International Conference on MultiMedia and Information Technology, MMIT 2008
Pages172-175
Number of pages4
DOIs
Publication statusPublished - 2008
Event2008 International Conference on MultiMedia and Information Technology, MMIT 2008 - Three Gorges, China
Duration: 30 Dec 200831 Dec 2008

Publication series

NameProceedings - 2008 International Conference on MultiMedia and Information Technology, MMIT 2008

Conference

Conference2008 International Conference on MultiMedia and Information Technology, MMIT 2008
Country/TerritoryChina
CityThree Gorges
Period30/12/0831/12/08

Keywords

  • Apriori algorithm
  • Content-based filtering
  • DF-RTF algorithm
  • Personalization recommendation model
  • Vector space model
  • Web access log mining

Fingerprint

Dive into the research topics of 'Mining web access log for the personalization recommendation'. Together they form a unique fingerprint.

Cite this