CIMR: intelligent cellphones recommendation for consumers based on machine learning techniques

Ruxun Xiang, Min Fu

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

Abstract

Cellphones are electronic devices that people use most in daily life. There are various kinds of cellphones in the market nowadays, while ordinary consumers are dazzled by the huge cellphone market and do not know how to choose a suitable one. Hence, it is of great importance to help them in doing this kind of selection to satisfy their needs. This paper proposes a machine learning based cellphones recommendation approach, CIMR, for consumers to pick up the most suitable cellphones that can mostly meet their desired requirements. We evaluate CIMR by using thousands of real consumers all over the world with two proposed methods: Gradient Boosting Decision Tree (GBDT) and Random Forest (RF). The experimental results show that the accuracy rate for recommending cellphones by using our method is 95.87%, which exceeds the existing old methods by up to 5.49%.
Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages104-108
Number of pages5
ISBN (Print)9781728146911
DOIs
Publication statusPublished - 2019
EventInternational Conference on Artificial Intelligence and Advanced Manufacturing (1st : 2019) - Dublin, Ireland
Duration: 17 Oct 201919 Oct 2019

Publication series

NameProceedings - 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019

Conference

ConferenceInternational Conference on Artificial Intelligence and Advanced Manufacturing (1st : 2019)
Abbreviated titleAIAM 2019
CountryIreland
CityDublin
Period17/10/1919/10/19

Keywords

  • Machine learning
  • Cellphone Recommendation
  • Data Mining

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