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Multimodal Price Prediction

Author

Listed:
  • Aidin Zehtab-Salmasi

    (University of Tabriz)

  • Ali-Reza Feizi-Derakhshi

    (University of Tabriz)

  • Narjes Nikzad-Khasmakhi

    (University of Tabriz)

  • Meysam Asgari-Chenaghlu

    (University of Tabriz)

  • Saeideh Nabipour

    (University of Mohaghegh Ardabili)

Abstract

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.

Suggested Citation

  • Aidin Zehtab-Salmasi & Ali-Reza Feizi-Derakhshi & Narjes Nikzad-Khasmakhi & Meysam Asgari-Chenaghlu & Saeideh Nabipour, 2023. "Multimodal Price Prediction," Annals of Data Science, Springer, vol. 10(3), pages 619-635, June.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-021-00326-z
    DOI: 10.1007/s40745-021-00326-z
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    References listed on IDEAS

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