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Insight monetization intermediary platform using recommender systems

Author

Listed:
  • Payam Hanafizadeh

    (Allameh Tabatab’i University)

  • Mahdi Barkhordari Firouzabadi

    (Allameh Tabatab’i University)

  • Khuong Minh Vu

    (National University of Singapore)

Abstract

Fundamental changes have prepared the grounds for a rapid movement towards becoming data and insight-driven. Businesses are continually seeking approaches to create more value from data. The main purpose of this article is to propose a model by which experts as Human Intelligence, can participate to share their expectations to orient the data processing towards the generation of insights needed to target industries and consequently, the realization of indirect data monetization. A set of recommendation systems as Artificial Intelligence, facilitate the submission and validation of expectations, access to data, and selling insights. The model also encompasses a direct data monetization strategy, wherein participants access or request their requirements in an Online Insight Marketplace. We have used the design science methodology to develop and validate our proposed model. The model is validated by comparison with competitive models from the literature, and also by bringing evidence from real-world applications which relate to the components of our model.

Suggested Citation

  • Payam Hanafizadeh & Mahdi Barkhordari Firouzabadi & Khuong Minh Vu, 2021. "Insight monetization intermediary platform using recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 269-293, June.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:2:d:10.1007_s12525-020-00449-w
    DOI: 10.1007/s12525-020-00449-w
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    1. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
    2. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    3. Ravi S. Sharma & Aijaz A. Shaikh & Eldon Li, 2021. "Designing Recommendation or Suggestion Systems: looking to the future," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 243-252, June.

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