Machine learning approach for finding business partners and building reciprocal relationships

被引:62
|
作者
Mori, Junichiro [1 ]
Kajikawa, Yuya [1 ]
Kashima, Hisashi [1 ]
Sakata, Ichiro [1 ]
机构
[1] Univ Tokyo, Bunkyo Ku, Tokyo 1138656, Japan
基金
日本学术振兴会;
关键词
Data mining; Support vector machine; Business development; SUPPORT VECTOR MACHINE; DATA-MINING APPROACH; SUPPLIER SELECTION; BANKRUPTCY PREDICTION; MANAGEMENT; STRATEGY; SYSTEM; VIEW;
D O I
10.1016/j.eswa.2012.01.202
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers and customers, and build reciprocal relationships among them, while it simultaneously offers many opportunities. In this contribution, we propose AI-based approach to find plausible candidates of business partners using firm profiles and transactional relationships among them. We employ machine learning techniques to build a prediction model of customer-supplier relationships. We applied our approach to the large amount of actual business data. The results showed that our approach successfully found potential business partners with F-values of about 84% and reciprocity among them with F-values of about 77%. Using our method, we also developed the Web-based system that helps people in actual businesses to find their new business partners. These contribute to developing one's own business in the complicated, specialized and rapidly changing business environments of recent years. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:10402 / 10407
页数:6
相关论文
共 50 条
  • [31] Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
    Erfanian, Sahar
    Zhou, Yewang
    Razzaq, Amar
    Abbas, Azhar
    Safeer, Asif Ali
    Li, Teng
    ENTROPY, 2022, 24 (10)
  • [32] Predicting Space Radiation Single Ion Exposure in Rodents: A Machine Learning Approach
    Prelich, Matthew T.
    Matar, Mona
    Gokoglu, Suleyman A.
    Gallo, Christopher A.
    Schepelmann, Alexander
    Iqbal, Asad K.
    Lewandowski, Beth E.
    Britten, Richard A.
    Prabhu, R. K.
    Myers, Jerry G., Jr.
    FRONTIERS IN SYSTEMS NEUROSCIENCE, 2021, 15
  • [33] Fault diagnosis studies of face milling cutter using machine learning approach
    Madhusudana, C. K.
    Budati, S.
    Gangadhar, N.
    Kumar, H.
    Narendranath, S.
    JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL, 2016, 35 (02) : 128 - 138
  • [34] Estimating energy consumption and GHG emissions in crop production: A machine learning approach
    Sharafi, Saeed
    Kazemi, Ali
    Amiri, Zahra
    JOURNAL OF CLEANER PRODUCTION, 2023, 408
  • [35] Machine learning approach of speech emotions recognition using feature fusion technique
    Paul, Bachchu
    Bera, Somnath
    Dey, Tanushree
    Phadikar, Santanu
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (03) : 8663 - 8688
  • [36] Democratizing business intelligence and machine learning for air traffic management safety
    Patriarca, R.
    Di Gravio, G.
    Cioponea, R.
    Licu, A.
    SAFETY SCIENCE, 2022, 146
  • [37] Assessing the drivers of machine learning business value
    Reis, Carolina
    Ruivo, Pedro
    Oliveira, Tiago
    Faroleiro, Paulo
    JOURNAL OF BUSINESS RESEARCH, 2020, 117 : 232 - 243
  • [38] Business data mining - a machine learning perspective
    Bose, I
    Mahapatra, RK
    INFORMATION & MANAGEMENT, 2001, 39 (03) : 211 - 225
  • [39] Urban building extraction using satellite imagery through Machine Learning
    Prakash, P. S.
    Soumya, K. D.
    Bharath, H. A.
    2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2018, : 1670 - 1675
  • [40] A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
    White, Diana T.
    Antoniou, Thibaud M.
    Martin, Jonathan M.
    Kmetz, William
    Twiss, Michael R.
    ECOLOGICAL APPLICATIONS, 2022, 32 (06)