An Interactive Machine Learning System for Image Advertisements

被引:0
|
作者
Foerste, Markus [1 ]
Nadj, Mario [2 ]
Knaeble, Merlin [2 ]
Maedche, Alexander [2 ]
Gehrmann, Leonie [3 ]
Stahl, Florian [3 ]
机构
[1] Collect Mind AG, Leonberg, Germany
[2] Karlsruhe Inst Technol, Inst Informat Syst & Mkt, Karlsruhe, Germany
[3] Univ Mannheim, Res Grp Quantitat Mkt & Consumer Analyt, Mannheim, Germany
来源
MENSCH AND COMPUTER 2021 (MUC 21) | 2021年
关键词
advertising; image ads; interactive machine learning;
D O I
10.1145/3473856.3474027
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Advertising is omnipresent in all countries around the world and has a strong influence on consumer behavior. Given that advertisements aim to be memorable, attract attention and convey the intended information in a limited space, it seems striking that previous research in economics and management has mostly neglected the content and style of actual advertisements and their evolution over time. With this in mind, we collected more than one million print advertisements from the English-language weekly news magazine "The Economist" from 1843 to 2014. However, there is a lack of interactive intelligent systems capable of processing such a vast amount of image data and allowing users to automatically and manually add metadata, explore images, find and test assertions, and use machine learning techniques they did not have access to before. Inspired by the research field of interactive machine learning, we propose such a system that enables domain experts like marketing scholars to process and analyze this huge collection of image advertisements.
引用
收藏
页码:574 / 577
页数:4
相关论文
共 50 条
  • [31] Explanatory Interactive Machine LearningEstablishing an Action Design Research Process for Machine Learning Projects
    Nicolas Pfeuffer
    Lorenz Baum
    Wolfgang Stammer
    Benjamin M. Abdel-Karim
    Patrick Schramowski
    Andreas M. Bucher
    Christian Hügel
    Gernot Rohde
    Kristian Kersting
    Oliver Hinz
    Business & Information Systems Engineering, 2023, 65 : 677 - 701
  • [32] Meta-Learning Initializations for Interactive Medical Image Registration
    Baum, Zachary M. C.
    Hu, Yipeng
    Barratt, Dean C.
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2023, 42 (03) : 823 - 833
  • [33] Activity recognition through interactive machine learning in a dynamic sensor setting
    Tegen, Agnes
    Davidsson, Paul
    Persson, Jan A.
    PERSONAL AND UBIQUITOUS COMPUTING, 2020, 28 (1) : 273 - 286
  • [34] Activity recognition through interactive machine learning in a dynamic sensor setting
    Agnes Tegen
    Paul Davidsson
    Jan A. Persson
    Personal and Ubiquitous Computing, 2024, 28 : 273 - 286
  • [35] Interactive Machine Learning Approach for Staff Selection Using Genetic Algorithm
    Ananthachari, Preethi
    Makhtumov, Nodirbek
    INTELLIGENT HUMAN COMPUTER INTERACTION, PT I, 2021, 12615 : 369 - 379
  • [36] Interactive Machine Learning for the Internet of Things: A Case Study on Activity Detection
    Tegen, Agnes
    Davidsson, Paul
    Persson, Jan A.
    PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON THE INTERNET OF THINGS ( IOT 2019), 2019,
  • [37] DrinkWatch: A Mobile Wellbeing Application Based on Interactive and Cooperative Machine Learning
    Flutura, Simon
    Seiderer, Andreas
    Asian, Ilhan
    Dang, Chi Tai
    Schwarz, Raphael
    Schiller, Dominik
    Andre, Elisabeth
    DH '18: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON DIGITAL HEALTH, 2018, : 65 - 74
  • [38] Power to the Oracle? Design Principles for Interactive Labeling Systems in Machine Learning
    Mario Nadj
    Merlin Knaeble
    Maximilian Xiling Li
    Alexander Maedche
    KI - Künstliche Intelligenz, 2020, 34 : 131 - 142
  • [39] Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction
    Senft, Emmanuel
    Lemaignan, Severin
    Baxter, Paul E.
    Belpaeme, Tony
    COMPANION OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17), 2017, : 281 - 282
  • [40] Using Interactive Machine Learning to Sonify Visually Impaired Dancers' Movement
    Katan, Simon
    MOCO'16: PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON MOVEMENT AND COMPUTING, 2016,