Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce

Zhang, Jianguo; Zou, Pengcheng; Zhao, Li; Wan, Yao; Pan, Xiuming; Gong, Yu; Yu, Philip S.

doi:10.18653/v1/n19-2009

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…Based on our online analysis, we find that products with recommendation reasons can receive more clicks than those without. Such effects of informative display have also been discussed in previous studies [7,10], showing that when generated properly, the recommendation reasons will increase the profits of the recommendation system. Given the product information, such as title, attributes and relevant QAs, RRG task aims at generating selling points of the product.…”

Section: Introductionmentioning

confidence: 53%

User-Inspired Posterior Network for Recommendation Reason Generation

Zhan

Zhang

Chen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user experience. A simple and effective way is to extract keywords directly from the knowledge-base of products, i.e., attributes or title, as the recommendation reason. However, generating recommendation reason from product knowledge doesn't naturally respond to users' interests. Fortunately, on some E-commerce websites, there exists more and more user-generated content (user-content for short), i.e., product question-answering (QA) discussions, which reflect user-cared aspects. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. In reality, adequate user-content is only possible for the most popular commodities, whereas large sums of long-tail products or new products cannot gather a sufficient number of user-content. To tackle this problem, we propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests with a posterior multiple QA discussions module, and generating recommendation reasons containing the product attributes as well as the user-cared aspects. Experimental results show that our model is superior to traditional generative models. Additionally, the analysis also shows that our model can focus more on the user-cared aspects than baselines. CCS CONCEPTS • Computing methodologies → Natural language processing; Natural language generation.

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Section: Introductionmentioning

confidence: 53%

User-Inspired Posterior Network for Recommendation Reason Generation

Zhan

Zhang

Chen

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Event title generation is to generate the titles of an event for enhancing product advertisement in our e-commerce platforms. Similar to the significant effect of the product titles on CTR and revenue (Zhang et al, 2019), the product event title has a crucial influence on the product's success. Event title generation is formulated as a sequenceto-sequence task to transform keywords describing the product characteristics into an impressive event title.…”

Section: Event Title Generationmentioning

confidence: 96%

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Kim¹,

Kim²,

Lee³

et al. 2021

Preprint

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GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce Hy-perCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of promptbased learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to nonexperts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.

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“…Sun et al [18] develops further on the work of Ptr-Net using a separate encoder network for important attributes, such as quantity and product brand, and then uses 2 pointers to decide where to copy data from. The work of Zhang et al [22] attempts a novel method of generating short descriptions for an e-commerce use case through multimodal information. A word sequence symbolizing the description and the image of a product in the catalog are chosen as descriptors, and are used to generate short titles for the product.…”

Section: Related Workmentioning

confidence: 99%

Generating Rich Product Descriptions for Conversational E-commerce Systems

Kedia

Mantha

Gupta

et al. 2021

Companion Proceedings of the Web Conference 2021

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Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce companies typically display short product titles on their webpages, either human-curated or algorithmically generated, when brevity is required. However, these titles are dissimilar from natural spoken language. For example, "Lucky Charms Gluten Free Break-fast Cereal, 20.5 oz a box Lucky Charms Gluten Free" is acceptable to display on a webpage, while a similar title cannot be used in a voice based text-to-speech application. In such conversational systems, an easy to comprehend sentence, such as "a 20.5 ounce box of lucky charms gluten free cereal" is preferred. Compared to display devices, where images and detailed product information can be presented to users, short titles for products which convey the most important information, are necessary when interfacing with voice assistants. We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles. Our extensive experiments on a real-world industry dataset, as well as human evaluation of model output, demonstrate that eBERT summarization outperforms comparable baseline models.Owing to the efficacy of the model, a version of this model has been deployed in real-world setting.

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Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce

Cited by 15 publications

References 21 publications

User-Inspired Posterior Network for Recommendation Reason Generation

User-Inspired Posterior Network for Recommendation Reason Generation

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Generating Rich Product Descriptions for Conversational E-commerce Systems

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