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.

中文翻译：

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AdsGNN：赞助者搜索中的行为图增强相关性建模

当人们在搜索引擎上搜索产品和服务时，赞助商搜索广告会显示在搜索结果旁边。近年来，它们已成为最赚钱的营销渠道之一。作为搜索广告的基本基础，由于重要的研究挑战和巨大的实用价值，相关性建模已引起越来越多的关注。大多数现有方法仅依赖于输入查询广告对中的语义信息，而短广告数据中的纯语义信息不足以完全识别用户的搜索意图。我们的动机在于，将历史搜索日志中的大量无人监督用户行为数据合并为补充图，以促进相关性建模。在本文中，我们广泛研究了如何将语义文本信息与用户行为图自然融合，并进一步提出了三种新颖的AdsGNN模型，以从节点，边缘和标记的角度聚合拓扑邻域。此外，对两个关键但很少研究的问题，即针对特定领域的预训练和长尾广告匹配，进行了深入研究。根据经验，我们在大型行业数据集上评估AdsGNN模型，在线/离线测试的实验结果始终证明了我们建议的优越性。被彻底研究。根据经验，我们在大型行业数据集上评估AdsGNN模型，在线/离线测试的实验结果始终证明了我们建议的优越性。被彻底研究。根据经验，我们在大型行业数据集上评估AdsGNN模型，在线/离线测试的实验结果始终证明了我们建议的优越性。