A Multimodal Late Fusion Model for E-Commerce Product Classification

• URL: http://arxiv.org/abs/2008.06179v1
• Date: Fri, 14 Aug 2020 03:46:24 GMT
• Title: A Multimodal Late Fusion Model for E-Commerce Product Classification
• Authors: Ye Bi, Shuo Wang, Zhongrui Fan
• Abstract summary: In this study, we investigated a multimodal late fusion approach based on text and image modalities to categorize e-commerce products on Rakuten. Specifically, we developed modal specific state-of-the-art deep neural networks for each input modal, and then fused them at the decision level. Our team named pa_curis won the 1st place with a macro-F1 of 0.9144 on the final leaderboard.
• Score: 7.463657960984954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The cataloging of product listings is a fundamental problem for most e-commerce platforms. Despite promising results obtained by unimodal-based methods, it can be expected that their performance can be further boosted by the consideration of multimodal product information. In this study, we investigated a multimodal late fusion approach based on text and image modalities to categorize e-commerce products on Rakuten. Specifically, we developed modal specific state-of-the-art deep neural networks for each input modal, and then fused them at the decision level. Experimental results on Multimodal Product Classification Task of SIGIR 2020 E-Commerce Workshop Data Challenge demonstrate the superiority and effectiveness of our proposed method compared with unimodal and other multimodal methods. Our team named pa_curis won the 1st place with a macro-F1 of 0.9144 on the final leaderboard.

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