Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, Zengxuan Wen


Abstract
Recently, sponsored search has become one of the most lucrative channels for marketing. As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value. Most existing methods solely rely on the query-keyword pairs. However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents. In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers’ information to bridge the gap between the search intents and advertising purposes. Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. We further propose a Bidding-Graph augmented Triple-based Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.
Anthology ID:
2021.findings-emnlp.191
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2215–2224
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.191
DOI:
10.18653/v1/2021.findings-emnlp.191
Bibkey:
Cite (ACL):
Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, and Zengxuan Wen. 2021. Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2215–2224, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search (Bi et al., Findings 2021)
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PDF:
https://aclanthology.org/2021.findings-emnlp.191.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.191.mp4