Fusion of multimodal data for clothing personalized recommendation with dynamic preference learning framework
Year: 2026
Type: article
Abstract: With the rapid expansion of e-commerce platforms, personalized clothing recommendation has emerged as a critical technology for improving user satisfaction and platform revenue. This study addresses the challenges of effectively integrating multimodal item information and capturing temporally evolving user preferences in clothing recommendation. We propose a Multimodal Fusion based Dynamic Preference Learning Framework (MF-DPL) that employs iterative cross-modal attention mechanisms and gated preference decomposition to construct a unified recommendation model. This approach successfully optimizes ranking accuracy in sparse implicit feedback scenarios, achieving substantial performance gains. Experimental results on the Amazon Clothing and Polyvore Outfits datasets show that MF-DPL improves Recall@10 by 8.2%-11.7% and Normalized Discounted Cumulative Gain (NDCG) @10 by 9.1%-12.4% compared to state of the art baselines. These findings provide practical guidance for multimodal personalized recommendation in fashion e-commerce and indicate new directions for dynamic preference modeling in trend sensitive domains.
Source: Discover Applied Sciences
Author: Ying Yuan
Institution: Kaifeng University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7170907308
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