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The impact of consumer preferences on the accuracy of collaborative filtering recommender systems

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  • Sebastian Köhler
  • Thomas Wöhner
  • Ralf Peters

Abstract

Despite the omnipresent use of recommender systems in electronic markets, previous research has not analyzed how consumer preferences affect the accuracy of recommender systems. Markets, however, are characterized by a certain structure of consumers’ preferences. Consequently, it is not known in which markets recommender systems perform well. In this paper, we introduce a microeconomic model that allows a systematical analysis of different structures of consumers’ preferences. We develop a model-specific metric to measure the recommendation accuracy. We employ our model in a simulation to evaluate the impact of the structure of the consumers’ preferences on the accuracy of a popular collaborative filtering algorithm. Our study shows that recommendation accuracy is significantly affected by the similarity and number of consumer types and the distribution of consumers. The investigation reveals that in certain markets even random product recommendations outperform the collaborative filtering algorithm.

Suggested Citation

  • Sebastian Köhler & Thomas Wöhner & Ralf Peters, 2016. "The impact of consumer preferences on the accuracy of collaborative filtering recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 369-379, November.
    • Handle: RePEc:spr:elmark:v:26:y:2016:i:4:d:10.1007_s12525-016-0232-3
    DOI: 10.1007/s12525-016-0232-3
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    Cited by:

    1. Zeshan Aslam Khan & Naveed Ishtiaq Chaudhary & Waqar Ali Abbasi & Sai Ho Ling & Muhammad Asif Zahoor Raja, 2023. "Design of Confidence-Integrated Denoising Auto-Encoder for Personalized Top-N Recommender Systems," Mathematics, MDPI, vol. 11(3), pages 1-25, February.
    2. Zeshan Aslam Khan & Naveed Ishtiaq Chaudhary & Syed Zubair, 2019. "Fractional stochastic gradient descent for recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 275-285, June.
    3. Rainer Alt & Hans-Dieter Zimmermann, 2016. "Electronic Markets on electronic markets in education," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 311-314, November.
    4. Khan, Zeshan Aslam & Chaudhary, Naveed Ishtiaq & Raja, Muhammad Asif Zahoor, 2022. "Generalized fractional strategy for recommender systems with chaotic ratings behavior," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    6. Jianshan Sun & Jian Song & Yuanchun Jiang & Yezheng Liu & Jun Li, 2022. "Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 101-121, March.
    7. 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.
    8. 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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    More about this item

    Keywords

    Recommender system; Consumer preferences; Recommendation accuracy; Collaborative filtering;
    All these keywords.

    JEL classification:

    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

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