Product Matching with Multimodal Integration for E-Commerce Price Intelligence
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Product matching is a fundamental challenge in
e-commerce, where the task is to determine whether two
product listings from different sources refer to the same real-
world item. Accurate identification is essential for price
monitoring, stock management, and competitive intelligence,
yet traditional approaches based on textual similarity or
structured identifiers often struggle with incomplete and
heterogeneous data. This paper presents RePrice, a hybrid
product matching system that integrates text, attribute, and
image similarity within a unified decision pipeline. The
approach combines deterministic rules with AI-driven scoring,
leveraging semantic embeddings and CLIP-based image
features. Experiments on 29,000 product listings from 23
platforms demonstrate that multimodal integration
significantly improves performance, achieving an F1-score of
0.75 compared to 0.42 for a text-only baseline. We analyze the
contribution of each modality, provide category-level results
with error analysis, and discuss the scalability and robustness of
the system for real-world e-commerce applications.