Learning consumer preferences through textual and visual data: a multi-modal approach

Xinyu Liu, Yezheng Liu, Yang Qian (), Yuanchun Jiang and Haifeng Ling
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Xinyu Liu: Hefei University of Technology
Yezheng Liu: Hefei University of Technology
Yang Qian: Hefei University of Technology
Yuanchun Jiang: Hefei University of Technology
Haifeng Ling: Hefei University of Technology

Electronic Commerce Research, 2025, vol. 25, issue 4, No 22, 2955-2984

Abstract: Abstract This paper proposes a novel multi-modal probabilistic topic model (LSTIT) to infer consumer preferences by jointly leveraging textual and visual data. Specifically, we use the title and image of the items purchased by consumers. Considering that the titles of items are relatively short text, we thus restrict the topic assignment for these titles. Meanwhile, we employ the same topic distribution to model the relationship between the title and the image of the item. To learn consumer preferences, the proposed model extracts several important dimensions based on textual words in titles and visual features in images. Experiments on the Amazon dataset show that the proposed model outperforms other baseline models for the task of learning consumer preferences. Our findings provide significant implications for managers to understand users’ personalized interests behind purchase behavior from a fine-grained level and a multi-modal perspective.

Keywords: User preferences; Multi-modal data; Topic model; Explainable learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10660-023-09780-8

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