Product Listing Ads (PLAs) are primary online advertisements merchants pay to attract more customers. However, merchants prefer to stack various attributes to the title and neglect the fluency and information priority. These seller-created titles are not suitable for PLAs as they fail to highlight the core information in the visible part in PLAs titles. In this work, we present a title rewrite solution. Specifically, we train a self-supervised language model to generate high-quality titles in terms of fluency and information priority. Extensive offline test and real-world online test have demonstrated that our solution is effective in reducing the cost and gaining more profit as it lowers our CPC, CPB while improving CTR in the online test by a large margin. It is also easy to train and deploy, which can be a best practice of title optimization for PLAs.
CITATION STYLE
Zhao, X., Liu, D., Ding, J., Yao, L., Yan, Y., Wang, H., & Yao, W. (2022). Self-supervised Product Title Rewrite for Product Listing Ads. In NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Industry Papers (pp. 79–85). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.naacl-industry.10
Mendeley helps you to discover research relevant for your work.