Commodity Recommendation for Users Based on E-commerce Data

被引：3
|
作者：
Yang, Fei [1 ]
Han, Xudong [1 ]
Lang, Jiying [2 ]
Lu, Weigang [3 ]
Liu, Lei [4 ]
Zhang, Lei [5 ]
Pan, Jingchang [2 ]
机构：
[1] Shandong Univ, Sch Mech Elect & Informat Engn, Room 512,180 Wenhua Xilu, Weihai 264209, Peoples R China
[2] Shandong Univ, Sch Mech Elect & Informat Engn, Room 510,180 Wenhua Xilu, Weihai 264209, Peoples R China
[3] Ocean Univ China, Dept Educ Technol, Room 110, Qingdao 266100, Peoples R China
[4] Chinese Acad Sci, Inst Acoust, 21 North 4th Ring Rd, Beijing 100190, Peoples R China
[5] Harbin Univ, Sch Art & Design, 109 Zhongxing Ave, Harbin 150086, Heilongjiang, Peoples R China
来源：
PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON BIG DATA RESEARCH (ICBDR 2018) | 2018年
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Mobile terminal; Consumption prediction; GBDT; Distributed platform;
D O I：
10.1145/3291801.3291803
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：146 / 149
页数：4
相关论文
共 50 条
  • [1] Commodity Recommendation System of E-commerce Based on Classification Users
    Chen, Jie
    [J]. MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY, 2014, 556-562 : 6689 - 6692
  • [2] An Advanced Recommendation System for E-commerce Users
    Tyagi, Ramkumari
    Jawdekar, Anand
    [J]. 2016 SYMPOSIUM ON COLOSSAL DATA ANALYSIS AND NETWORKING (CDAN), 2016,
  • [3] E-Commerce Commodity Recommendation System Based on Social Perception and Mobile Computing
    Deng, Qianqian
    Guo, Yuan
    Meng, Tan
    [J]. MOBILE NETWORKS & APPLICATIONS, 2023,
  • [4] Personalized Recommendation Model for Mobile E-commerce Users
    Zhang Xiaona
    [J]. 2021 13TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2021), 2021, : 707 - 710
  • [5] Big Data Based E-commerce Search Advertising Recommendation
    Tao, Ming
    Huang, Peican
    Li, Xueqiang
    Ding, Kai
    [J]. CYBERSPACE SAFETY AND SECURITY, PT I, 2020, 11982 : 457 - 466
  • [6] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
    Wang, Jizhe
    Huang, Pipei
    Zhao, Huan
    Zhang, Zhibo
    Zhao, Binqiang
    Lee, Dik Lun
    [J]. KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2018, : 839 - 848
  • [7] AN INVESTMENT PORTFOLIO RECOMMENDATION SYSTEM FOR INDIVIDUAL E-COMMERCE USERS
    Li, Xiang
    Yu, Chunxia
    [J]. 24TH INTERNATIONAL CONFERENCE ON PRODUCTION RESEARCH (ICPR), 2017, : 580 - 585
  • [8] Personalized recommendation system of e-commerce based on big data analysis
    Chen, Hua
    [J]. JOURNAL OF INTERDISCIPLINARY MATHEMATICS, 2018, 21 (05) : 1243 - 1247
  • [9] Life Stage Based Recommendation in E-commerce
    Guo, Bin
    Dou, Kai
    Kuang, Li
    [J]. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 3461 - 3468
  • [10] Content-based recommendation in E-commerce
    Xu, B
    Zhang, MM
    Pan, ZG
    Yang, HW
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, PT 2, 2005, 3481 : 946 - 955
12345