LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising
In e-commerce advertising, ad platforms typically fail to model the externalities among advertisers when making optimization decisions: an advertiser's actual performance is largely shaped by rivals competing for the same users; these rivals typically operate in overlapping product categories and…
This paper introduces LACRM, a framework that uses large language models (LLMs) to explicitly model directed competition relationships between advertisers in e-commerce platforms, addressing limitations in existing ad optimization systems.
What happened
The article presents LACRM, a novel framework that addresses the challenge of modeling directed advertiser competition in e-commerce advertising systems through LLM-driven techniques, demonstrating measurable improvements in ad revenue through industrial experiments.
Why it matters
Explicit competition modeling improves ad platform optimization by accounting for rival dynamics, which directly impacts revenue generation and resource allocation in large-scale advertising systems.
- Directed competition modeling improves ad platform performance
- LLMs enable more nuanced analysis of asymmetric advertiser relationships
- Temporal auction behavior alignment enhances competition intensity modeling
- Latent rival identification reflects real market dynamics, not just model artifacts
The bigger picture
The framework demonstrates practical value in industrial advertising systems, with measurable revenue gains that could influence future ad platform design and competitive analysis methodologies.
LACRM provides a novel approach to modeling advertiser competition in e-commerce advertising through LLM-driven techniques, achieving measurable business impact while addressing limitations in existing methods.
Technical details
- Three-stage pipeline for constructing directed competition graphs
- LLM-driven role-separated profiling for asymmetric relationship analysis
- Listwise learning-to-rank aligned with temporal auction behavior
- Continuous edge intensity representation in competition graphs
FAQ
What problem does LACRM solve in e-commerce advertising?
LACRM addresses the challenge of modeling directed competition relationships between advertisers, which existing systems fail to capture effectively.
How does LACRM differ from traditional approaches?
Unlike symmetric similarity models, LACRM uses LLM-driven role-separated profiling for asymmetric multi-route retrieval and listwise learning-to-rank with temporal auction behavior.
What are the practical benefits of LACRM?
LACRM-guided bidding and incentive allocation increased advertising revenue by 1.73% and 1.14% respectively in production tests.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 14, 2026.
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