Product recommendation system for small online retailers using association rules mining

Junnan Chen, Courtney Miller, Gaby G. Dagher

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

19 Scopus citations

Abstract

Recommendation systems in e-commerce have become essential tools to help businesses increase their sales. In this paper, we detail the design of a product recommendation system for small online retailers. Our system is specifically designed to address the needs of retailers with small data pools and limited processing power, and is tested for accuracy, efficiency, and scalability on real life data from a small online retailer.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Conference on Innovative Design and Manufacturing, ICIDM 2014
EditorsYong Zeng, Yong Chen, Yong Chen, Sofiane Achiche, Weiming Shen, Anjali Awasthi, Chih-Hsing Chu, Caterina Rizzi, Chun Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-77
Number of pages7
ISBN (Electronic)9781479962709
DOIs
StatePublished - 26 Sep 2014
Event2014 International Conference on Innovative Design and Manufacturing, ICIDM 2014 - Montreal, Canada
Duration: 13 Aug 201415 Aug 2014

Publication series

NameProceedings of the 2014 International Conference on Innovative Design and Manufacturing, ICIDM 2014

Conference

Conference2014 International Conference on Innovative Design and Manufacturing, ICIDM 2014
Country/TerritoryCanada
CityMontreal
Period13/08/1415/08/14

Keywords

  • Data Mining
  • Database Management
  • E-commerce
  • Performance

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