# Domain-Constrained Advertising Keyword Generation

arXiv: https://arxiv.org/abs/1902.10374 · HTML: https://mlsanity.com/paper/1902.10374/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [Diversity driven Query Rewriting in Search Advertising](https://mlsanity.com/paper/2106.03816/) (2021)
- [Triangular Bidword Generation for Sponsored Search Auction](https://mlsanity.com/paper/2101.11349/) (2021)
- [From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning](https://mlsanity.com/paper/2408.08981/) (2024)
- [VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search](https://mlsanity.com/paper/2504.05321/) (2025)
- [Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2302.12372/) (2023)
- [OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising](https://mlsanity.com/paper/2412.03577/) (2024)
- [KP-Agent: Keyword Pruning in Sponsored Search Advertising via LLM-Powered Contextual Bandits](https://mlsanity.com/paper/2601.05257/) (2025)

## Abstract (arXiv)

Advertising (ad for short) keyword suggestion is important for sponsored search to improve online advertising and increase search revenue. There are two common challenges in this task. First, the keyword bidding problem: hot ad keywords are very expensive for most of the advertisers because more advertisers are bidding on more popular keywords, while unpopular keywords are difficult to discover. As a result, most ads have few chances to be presented to the users. Second, the inefficient ad impression issue: a large proportion of search queries, which are unpopular yet relevant to many ad keywords, have no ads presented on their search result pages. Existing retrieval-based or matching-based methods either deteriorate the bidding competition or are unable to suggest novel keywords to cover more queries, which leads to inefficient ad impressions. To address the above issues, this work investigates to use generative neural networks for keyword generation in sponsored search. Given a purchased keyword (a word sequence) as input, our model can generate a set of keywords that are not only relevant to the input but also satisfy the domain constraint which enforces that the domain category of a generated keyword is as expected. Furthermore, a reinforcement learning algorithm is proposed to adaptively utilize domain-specific information in keyword generation. Offline evaluation shows that the proposed model can generate keywords that are diverse, novel, relevant to the source keyword, and accordant with the domain constraint. Online evaluation shows that generative models can improve coverage (COV), click-through rate (CTR), and revenue per mille (RPM) substantially in sponsored search.