# Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

arXiv: https://arxiv.org/abs/2210.15459 · HTML: https://mlsanity.com/paper/2210.15459/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising](https://mlsanity.com/paper/2103.10246/) (2021)
- [Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns](https://mlsanity.com/paper/2003.01452/) (2020)
- [Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising](https://mlsanity.com/paper/2006.16312/) (2020)
- [Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising](https://mlsanity.com/paper/1607.01869/) (2016)
- [Triangular Bidword Generation for Sponsored Search Auction](https://mlsanity.com/paper/2101.11349/) (2021)
- [Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework](https://mlsanity.com/paper/2202.13506/) (2022)
- [Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study](https://mlsanity.com/paper/2203.02200/) (2022)
- [Optimal Keywords Grouping in Sponsored Search Advertising under Uncertain Environments](https://mlsanity.com/paper/2203.02192/) (2022)
- [Optimizing AD Pruning of Sponsored Search with Reinforcement Learning](https://mlsanity.com/paper/2008.02014/) (2020)
- [Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth](https://mlsanity.com/paper/2202.13566/) (2022)

## Built on by (most cited first, 4 of 4)

- [Causality-based CTR Prediction using Graph Neural Networks](https://mlsanity.com/paper/2301.12762/) (2023)
- [Keyword Decisions in Sponsored Search Advertising: A Literature Review and Research Agenda](https://mlsanity.com/paper/2302.12372/) (2023)
- [BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations](https://mlsanity.com/paper/2505.19164/) (2025)
- [ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising](https://mlsanity.com/paper/2410.22233/) (2024)

## Abstract (arXiv)

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.