research-article

Commodity Recommendation for Users Based on E-commerce Data

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Lei Liu,

Jingchang PanAuthors Info & Claims

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

Pages 146 - 149

https://doi.org/10.1145/3291801.3291803

Published: 27 October 2018 Publication History

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Abstract

With the popularity of mobile devices and the development of e-commerce, more and more people choose to buy items in the mobile terminal. Therefore the mobile terminal commodity recommendation services and commodity recommendation algorithms are more and more important. Aim at this problem, this paper conducts a study of predicting the user's purchase behavior based on the online distribution platform and the desensitization data sets provided by the Chinese largest electricity platform Alibaba. Based on the GBDT (Gradient Boosting Decision Tree) model, by using ODPS (Open Data Processing Service) and Python to simultaneously implement machine learning and training online and offline respectively, and combining with the user behavior sequence recorded over a period of time, the user purchase behavior at a later time will be properly predicted.

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Purnomo ASusanti TAnisah HSari AMaulana F(2021)Value of M-Commerce Research: A Bibliometric Perspective2021 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech53080.2021.9534928(813-818)Online publication date: 19-Aug-2021
https://doi.org/10.1109/ICIMTech53080.2021.9534928
Micol Policarpo Lda Silveira Dda Rosa Righi RAntunes Stoffel Rda Costa CVictória Barbosa JScorsatto RArcot T(2021)Machine learning through the lens of e-commerce initiativesComputer Science Review10.1016/j.cosrev.2021.10041441:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100414

Index Terms

Commodity Recommendation for Users Based on E-commerce Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
        Boosting

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ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

October 2018

221 pages

ISBN:9781450364768

DOI:10.1145/3291801

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shandong Univ.: Shandong University
University of Queensland: University of Queensland
Dalian Maritime University

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Association for Computing Machinery

New York, NY, United States

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Published: 27 October 2018

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China Postdoctoral Science Foundation
Natural Science Foundation of Shandong Province
National Natural Science Foundation of China
MOE (Ministry of Education in China) Project of Humanities and Social Sciences

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ICBDR 2018

ICBDR 2018: 2018 The 2nd International Conference on Big Data Research

October 27 - 29, 2018

Weihai, China

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Purnomo ASusanti TAnisah HSari AMaulana F(2021)Value of M-Commerce Research: A Bibliometric Perspective2021 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech53080.2021.9534928(813-818)Online publication date: 19-Aug-2021
https://doi.org/10.1109/ICIMTech53080.2021.9534928
Micol Policarpo Lda Silveira Dda Rosa Righi RAntunes Stoffel Rda Costa CVictória Barbosa JScorsatto RArcot T(2021)Machine learning through the lens of e-commerce initiativesComputer Science Review10.1016/j.cosrev.2021.10041441:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100414

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Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Personalized hybrid recommendation for group of users

E-Commerce Recommendation Applications

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Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Personalized hybrid recommendation for group of users

E-Commerce Recommendation Applications

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