Implementation of weighted parallel hybrid recommender systems for e-commerce in Indonesia

Mustika Aprilianti, Rahmad Mahendra, Indra Budi

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

5 Citations (Scopus)

Abstract

This paper focus on building recommender system with weighted parallel hybrid method for e-commerce in Indonesia. The dataset was derived from one of the largest ecommerce company in Indonesia. The experiments used three sampling techniques, namely bootstrapping validation, timing series and systematic sampling. The best result of these experiments yields F1-measure of 9.99% which is achieved by the combination of user-based collaborative filtering approach and content-based filtering approach. Moreover, the value of evaluation metrics in this research is not much different from the previous research of recommender system. This indicates that recommender systems can be applied to e-commerce companies in Indonesia.

Original languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages321-326
Number of pages6
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
CountryIndonesia
CityMalang
Period15/10/1616/10/16

Keywords

  • Collaborative Filtering
  • Content-based Filtering
  • E-commerce
  • Recommender Systems
  • Weighted Parallel Hrbrid

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