Sequential pattern mining for e-commerce recommender system

Ridho Trivonanda, Rahmad Mahendra, Indra Budi, Rani Aulia Hidayat

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

Abstract

Recommender system is one of the strategies carried out by e-commerce to increase their users' satisfaction. In this paper, we implement sequence pattern mining for recommender system in e-commerce domain. We perform the PrefixSpan algorithm to mine the frequent patterns. The frequent patterns generate the suitable rules for the recommender system. The experiments reported in this paper utilize the datasets from two different marketplaces in Indonesia. The best performance of recommendation model is obtained when applying the rules derived from the product category with minimum confidence of 0.5.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages393-398
Number of pages6
ISBN (Electronic)9781728192796
DOIs
Publication statusPublished - 17 Oct 2020
Event12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 - Virtual, Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020

Conference

Conference12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Country/TerritoryIndonesia
CityVirtual, Depok
Period17/10/2018/10/20

Keywords

  • e-Commerce
  • PrefixSpan
  • Recommender Systems
  • Sequential Pattern Mining

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