Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
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DOI：
10.18653/v1/n19-2009
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发表时间：
2019-04
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通讯作者：
Jianguo Zhang;Pengcheng Zou;Zhao Li;Yao Wan;Xiuming Pan;Yu Gong;Philip S. Yu
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作者：
Jianguo Zhang;Pengcheng Zou;Zhao Li;Yao Wan;Xiuming Pan;Yu Gong;Philip S. Yu
如今，越来越多的消费者喜欢使用淘宝、亚马逊等移动电子商务应用程序浏览和购买产品。由于商家通常倾向于描述冗余且信息过多的产品标题来吸引顾客的注意力，因此在有限的手机屏幕上简洁地显示简短的产品标题非常重要。为了解决这种差异，以前的研究主要考虑长产品标题的文本信息，并且在培训和评估过程中缺乏类人视图。在本文中，我们提出了一种用于电子商务中短产品标题生成的多模态生成对抗网络（MM-GAN），它创新性地结合了产品的图像信息和属性标签以及原始长标题的文本信息。 MM-GAN 将短标题生成视为强化学习过程，其中生成的标题由判别器以类似人类的视角进行评估。对大规模电子商务数据集的大量实验表明，我们的算法优于其他最先进的方法。此外，我们将模型部署到真实的在线电子商务环境中，有效地将点击率和点击转化率分别提高了 1.66% 和 1.87%。
Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation in E-Commerce, which innovatively incorporates image information and attribute tags from product, as well as textual information from original long titles. MM-GAN poses short title generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view. Extensive experiments on a large-scale E-Commerce dataset demonstrate that our algorithm outperforms other state-of-the-art methods. Moreover, we deploy our model into a real-world online E-Commerce environment and effectively boost the performance of click through rate and click conversion rate by 1.66% and 1.87%, respectively.