# Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

arXiv: https://arxiv.org/abs/1607.01869 · HTML: https://mlsanity.com/paper/1607.01869/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2016.

## Built on by (most cited first, 12 of 12)

- [Similarity search in the blink of an eye with compressed indices](https://mlsanity.com/paper/2304.04759/) (2023)
- [Diversity driven Query Rewriting in Search Advertising](https://mlsanity.com/paper/2106.03816/) (2021)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [Ad Insertion in LLM-Generated Responses](https://mlsanity.com/paper/2601.19435/) (2026)
- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search](https://mlsanity.com/paper/2504.05321/) (2025)
- [Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching](https://mlsanity.com/paper/2210.15459/) (2022)
- [Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2302.12372/) (2023)
- [Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning](https://mlsanity.com/paper/2505.18897/) (2025)
- [GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction](https://mlsanity.com/paper/2410.22347/) (2024)
- [Individualized non-uniform quantization for vector search](https://mlsanity.com/paper/2509.18471/) (2025)

## Abstract (arXiv)

Sponsored search represents a major source of revenue for web search engines. This popular advertising model brings a unique possibility for advertisers to target users' immediate intent communicated through a search query, usually by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries it is challenging for advertisers to identify all such relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advanced matching approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage, and incremental revenue. Lastly, we open-source learned query embeddings to be used by researchers in computational advertising and related fields.