  Sponsored search ads appear next to search results when people look for
products and services on search engines. In recent years, they have become one
of the most lucrative channels for marketing. As the fundamental basis of
search ads, relevance modeling has attracted increasing attention due to the
significant research challenges and tremendous practical value. Most existing
approaches solely rely on the semantic information in the input query-ad pair,
while the pure semantic information in the short ads data is not sufficient to
fully identify user's search intents. Our motivation lies in incorporating the
tremendous amount of unsupervised user behavior data from the historical search
logs as the complementary graph to facilitate relevance modeling. In this
paper, we extensively investigate how to naturally fuse the semantic textual
information with the user behavior graph, and further propose three novel
AdsGNN models to aggregate topological neighborhood from the perspectives of
nodes, edges and tokens. Furthermore, two critical but rarely investigated
problems, domain-specific pre-training and long-tail ads matching, are studied
thoroughly. Empirically, we evaluate the AdsGNN models over the large industry
dataset, and the experimental results of online/offline tests consistently
demonstrate the superiority of our proposal.
