# RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search

arXiv: https://arxiv.org/abs/1910.12527 · HTML: https://mlsanity.com/paper/1910.12527/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search](https://mlsanity.com/paper/2504.05321/) (2025)
- [Multi-Intent Attribute-Aware Text Matching in Searching](https://mlsanity.com/paper/2402.07788/) (2024)
- [Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching](https://mlsanity.com/paper/2507.05617/) (2025)
- [Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style](https://mlsanity.com/paper/2502.15616/) (2025)
- [Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising](https://mlsanity.com/paper/2506.03827/) (2025)

## Abstract (arXiv)

Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM). Existing sponsored search models are all based on traditional statistical models, which have poor RPM performance when queries follow a heavy-tailed distribution. Here, we propose an RPM-oriented Query Rewriting Framework (RQRF) which outputs related bid keywords that can yield high RPM. RQRF embeds both queries and bid keywords to vectors in the same implicit space, converting the rewriting probability between each query and keyword to the distance between the two vectors. For label construction, we propose an RPM-oriented sample construction method, labeling keywords based on whether or not they can lead to high RPM. Extensive experiments are conducted to evaluate performance of RQRF. In a one month large-scale real-world traffic of e-commerce sponsored search system, the proposed model significantly outperforms traditional baseline.