论文标题
电子商务产品分类的多模式晚期融合模型
A Multimodal Late Fusion Model for E-Commerce Product Classification
论文作者
论文摘要
对于大多数电子商务平台来说，产品清单的分类是一个基本问题。尽管通过基于单峰的方法获得了有希望的结果，但可以预期，通过考虑多模式产品信息可以进一步提高其性能。在这项研究中，我们根据文本和图像方式研究了一种多模式的晚期融合方法，以对Rakuten进行电子商务产品进行分类。具体而言，我们为每个输入模式开发了模态特定的最新深神网络，然后将它们融合到决策级别。 Sigir 2020电子商务研讨会数据挑战的多模式产品分类任务的实验结果证明了我们所提出方法的优势和有效性与单峰和其他多模式方法相比。我们的名为PA_Curis的团队在最终排行榜上以0.9144的宏F1赢得了第一名。
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.
