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Cited by 7Information Retrieval and Search BehaviorTopic ModelingDigital Marketing and Social Media

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CHASE: Commonsense-Enriched Advertising on Search Engine with Explicit Knowledge

Zhang, Chao, Zhou, Jingbo, Zang, Xiaoling, Xu, Qing, Yin, Liang, He, Xiang, Liu, Lin, Xiong, Haoyi, Dou, Dejing·2021·Proceedings of the 30th ACM International Conference on Information & Knowledge Management
DOI 10.1145/3459637.3481902
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Abstract

While online advertising is one of the major sources of income for search engines, pumping up the incomes from business advertisements while ensuring the user experience becomes a challenging but emerging area. Designing high-quality advertisements with persuasive content has been proved as a way to increase revenues through improving the Click-Through Rate (CTR). However, it is difficult to scale up the design of high-quality ads, due to the lack of automation in creativity. In this paper, we present Commonsense-Enriched Advertisement on Search Engine (CHASE) --- a system for the automatic generation of persuasive ads. CHASE adopts a specially designed language model that fuses the keywords, commonsense-related texts, and marketing contents to generate persuasive advertisements. Specifically, the language model has been pre-trained using massive contents of explicit knowledge and fine-tuned with well-constructed quasi-parallel corpora with effective control of the proportion of commonsense in the generated ads and fitness to the ads' keywords. The effectiveness of the proposed method CHASE has been verified by real-world web traffics for search and manual evaluation. In A/B tests, the advertisements generated by CHASE would bring 11.13% CTR improvement. The proposed model has been deployed to cover three advertisement domains (which are kid education, psychological counseling, and beauty e-commerce) at Baidu, the world's largest Chinese search engine, with adding revenue of about 1 million RMB (Chinese Yuan) per day.

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Metadata

Authors
Zhang, C., Zhou, J., Zang, X., Xu, Q., Yin, L., He, X., Liu, L., Xiong, H., & Dou, D.
Affiliations
Baidu (China) (CN)
ORCID
Venue
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
Publisher
ACM
Published
2021-10-26
Pages
4352-4361
ISSN
2155-0751 (ISSN-L)
DOI
10.1145/3459637.3481902
OpenAlex
W3209034179
Cited by
7
References
49
Citations by year
2026: 2 · 2024: 2 · 2023: 2 · 2022: 1
FWCI
1.04
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
Keywords
persuasive advertising, search engine advertising, click-through rate, language model fine-tuning, online advertising revenue, Chinese search engines

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CoChat. (2026). Citation check: CHASE: Commonsense-Enriched Advertising on Search Engine with Explicit Knowledge. https://app.cochat.ai/papers/10.1145/3459637.3481902
The paper
Zhang, C., Zhou, J., Zang, X., Xu, Q., Yin, L., He, X., Liu, L., Xiong, H., & Dou, D. (2021). CHASE: Commonsense-Enriched Advertising on Search Engine with Explicit Knowledge. Proceedings of the 30th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3459637.3481902