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Aspect-Based Recommendation Model for Fashion Merchandising

In: Advances in Digital Marketing and eCommerce

Author

Listed:
  • Weiqing Li

    (Xi’an Polytechnic University)

  • Bugao Xu

    (University of North Texas)

Abstract

An aspect-based recommendation model (ARM) was proposed to detect local and global aspect representations in customer reviews available on ecommerce websites for fashion merchandising. This model was constructed with two independent paths to process user/item reviews simultaneously, and each path had a convolutional neural network (CNN), a long-short time memory network (LSTM) with attention mechanism to separately capture local aspect features and global aspect features. To enhance the generalization of the ARM model, the local and global aspect features from both user and item reviews were merged through mutual operations prior to the rating prediction. The Clothing, Shoes & Jewelry dataset from Amazon 5-core was used to train and test ARM. The significance of the extracted aspects regarding user preferences and item properties from the reviews were examined as opposed to several state-of-the-art fashion recommenders.

Suggested Citation

  • Weiqing Li & Bugao Xu, 2021. "Aspect-Based Recommendation Model for Fashion Merchandising," Springer Proceedings in Business and Economics, in: Francisco J. Martínez-López & David López López (ed.), Advances in Digital Marketing and eCommerce, pages 243-250, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-76520-0_25
    DOI: 10.1007/978-3-030-76520-0_25
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