Triangular Bidword Generation for Sponsored Search Auction
This work addresses data noise issues in sponsored search auctions for advertisers and search engines, but it is incremental as it builds on existing methods by introducing a joint training framework.
The paper tackles the problem of noisy training data in bidword generation for sponsored search auctions by proposing TRIDENT, a triangular model that uses high-quality query-advertisement pairs to indirectly supervise bidword generation, resulting in relevant and diverse bidwords validated through automatic, human, and online evaluations.
Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in <query, bidword> and <advertisement, bidword> pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired <query, advertisement> as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.