# AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search

arXiv: https://arxiv.org/abs/2104.12080 · HTML: https://mlsanity.com/paper/2104.12080/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2021.

## Built on by (most cited first, 30 of 38)

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- [Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search](https://mlsanity.com/paper/2202.06212/) (2022)
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- [Pretraining Language Models with Text-Attributed Heterogeneous Graphs](https://mlsanity.com/paper/2310.12580/) (2023)
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- [Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs](https://mlsanity.com/paper/2407.15431/) (2024)
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- [Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning](https://mlsanity.com/paper/2406.13235/) (2024)
- [Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting](https://mlsanity.com/paper/2408.09439/) (2024)
- [Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection](https://mlsanity.com/paper/2503.21127/) (2025)
- [Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning](https://mlsanity.com/paper/2308.05379/) (2023)
- [Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search](https://mlsanity.com/paper/2309.16141/) (2023)
- [Test-Time Training on Graphs with Large Language Models (LLMs)](https://mlsanity.com/paper/2404.13571/) (2024)
- [Parameter-Efficient Tuning Large Language Models for Graph Representation Learning](https://mlsanity.com/paper/2404.18271/) (2024)
- [Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models](https://mlsanity.com/paper/2405.18581/) (2024)

## Abstract (arXiv)

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.