Comparison of Deep Learning Models for Automatic Generation of Product Description on E-commerce site
Comparison of Deep Learning Models for Automatic Generation of Product Description on E-commerce site
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DOI：
10.1145/3487664.3487696
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Kenji Fukumoto;Rinji Suzuki;Hiroyuki Terada;Masafumi Bato;Akiyo Nadamoto
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文献类型：
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作者：
Kenji Fukumoto;Rinji Suzuki;Hiroyuki Terada;Masafumi Bato;Akiyo Nadamoto
人们可以很容易地在电子商务网站上发布他们的产品。当用户在电子商务网站上展示他们的产品时，他们必须创建一个描述该产品并鼓励购买的文档。然而，对于初学者来说，创建一个描述产品的句子并不容易。在本研究中，描述产品的句子称为产品描述。我们提出了一种基于LSTM和GPT-2的比较自动生成产品描述的方法。具体来说，我们检查现有产品描述中包含的产品的数据结构。然后，我们使用基于这些数据结构的数据作为输入来比较这两种方法。此外，我们进行了两个实验来衡量我们提出的方法的好处，我们提出的评估指标的基础上。
People can readily post their products on e-commerce sites. When users present their product on an e-commerce site, they must create a document describing the item and encouraging its purchase. However, it is not easy for beginner users to create a sentence that describes a product. For this study, the sentence describing the product is called the product description. We propose a method for automatically generating the product description based on a comparison of LSTM and GPT-2. Specifically, we examine the data structure of the product included in the existing product description. Then we use data based on these data structures as input to compare these two methods. Furthermore, we conduct two experiments to measure the benefits of our proposed method based on our proposed evaluation index.