Abstract
Advertisement (Ad) title plays a significant role in the effectiveness of online commercial advertising. However, it’s difficult for most advertisers to think of attractive titles for their products. By mining keywords from current ad material, traditional retrieval methods and neural text generation models have been applied to solve this problem. However, few of them focus on personalized ad titles generation. Ad titles from different advertisers can be very diversified, and there is massive previous advertising data available, which can tell the style, content, and vocabulary of specific advertisers. Based on massive previous advertising data and current ad material, we propose an Ad-Profile-based Title Generation Network (APTGN) to automatically generate personalized titles for ads. The model utilizes massive advertising data and current ad material to construct a profile for each ad, which is further integrated into the generation model to help recognize the preferences of specific ads. Automatic evaluation metrics and online A/B testing both show that our model significantly outperforms all the baselines, increasing the adoption rate of recommendation titles by 27.22%. Through our deployed model, once an advertiser needs to customize an ad title for their products, satisfactory titles can be recommended automatically without bothering to write any words.
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Acknowledgments
This work is supported in part by grants JC2017005, HW2020004, 20AZD085 and 2020A1515010949. We thank the anonymous reviewers for their valuable comments.
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Wang, J. et al. (2021). Generating Personalized Titles Incorporating Advertisement Profile. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_37
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DOI: https://doi.org/10.1007/978-3-030-73200-4_37
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