A recommendation algorithm using multi-level association rules

Choonho Kim, Juntae Kim

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

56 Scopus citations

Abstract

Recommendation systems predict user's preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user's known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.

Original languageEnglish
Title of host publicationProceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003
EditorsJiming Liu, Nick Cercone, Matthias Klusch, Chunnian Liu, Ning Zhong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages524-527
Number of pages4
ISBN (Electronic)0769519326, 9780769519326
DOIs
StatePublished - 2003
EventIEEE/WIC International Conference on Web Intelligence, WI 2003 - Halifax, Canada
Duration: 13 Oct 200317 Oct 2003

Publication series

NameProceedings - IEEE/WIC International Conference on Web Intelligence, WI 2003

Conference

ConferenceIEEE/WIC International Conference on Web Intelligence, WI 2003
Country/TerritoryCanada
CityHalifax
Period13/10/0317/10/03

Keywords

  • Association rules
  • Bayesian methods
  • Collaboration
  • Data mining
  • Filtering algorithms
  • Information analysis
  • Performance analysis
  • Predictive models
  • Scalability
  • Web pages

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