# BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

arXiv: https://arxiv.org/abs/2505.19164 · HTML: https://mlsanity.com/paper/2505.19164/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)
- [NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://mlsanity.com/paper/2207.04452/) (2022)
- [Topological Sort for Sentence Ordering](https://mlsanity.com/paper/2005.00432/) (2020)
- [A Survey on Evaluation of Out-of-Distribution Generalization](https://mlsanity.com/paper/2403.01874/) (2024)
- [A Survey on Out-of-Distribution Evaluation of Neural NLP Models](https://mlsanity.com/paper/2306.15261/) (2023)
- [Open Vocabulary Extreme Classification Using Generative Models](https://mlsanity.com/paper/2205.05812/) (2022)
- [Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao](https://mlsanity.com/paper/2306.03527/) (2023)
- [Learning Better Internal Structure of Words for Sequence Labeling](https://mlsanity.com/paper/1810.12443/) (2018)
- [Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation](https://mlsanity.com/paper/2407.20462/) (2024)
- [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)
- [Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching](https://mlsanity.com/paper/2210.15459/) (2022)
- [To Judge or not to Judge: Using LLM Judgements for Advertiser Keyphrase Relevance at eBay](https://mlsanity.com/paper/2505.04209/) (2025)
- [QueryNER: Segmentation of E-commerce Queries](https://mlsanity.com/paper/2405.09507/) (2024)
- [GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation](https://mlsanity.com/paper/2409.03140/) (2024)
- [On the Robustness of Generative Information Retrieval Models](https://mlsanity.com/paper/2412.18768/) (2024)
- [Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search](https://mlsanity.com/paper/2502.00131/) (2025)
- [UniDEC : Unified Dual Encoder and Classifier Training for Extreme Multi-Label Classification](https://mlsanity.com/paper/2405.03714/) (2024)

## Built on by (most cited first, 1 of 1)

- [LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay](https://mlsanity.com/paper/2508.03628/) (2025)

## Abstract (arXiv)

In the domain of sponsored search advertising, the focus of Keyphrase recommendation has largely been on exact match types, which pose issues such as high management expenses, limited targeting scope, and evolving search query patterns. Alternatives like Broad match types can alleviate certain drawbacks of exact matches but present challenges like poor targeting accuracy and minimal supervisory signals owing to limited advertiser usage. This research defines the criteria for an ideal broad match, emphasizing on both efficiency and effectiveness, ensuring that a significant portion of matched queries are relevant. We propose BroadGen, an innovative framework that recommends efficient and effective broad match keyphrases by utilizing historical search query data. Additionally, we demonstrate that BroadGen, through token correspondence modeling, maintains better query stability over time. BroadGen's capabilities allow it to serve daily, millions of sellers at eBay with over 2.5 billion items.