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 at comparable budget scales.
However, capturing this rivalry as an explicit, reusable signal is challenging because it is directed, multi-faceted, and partially observable.
Existing paradigms such as interaction graphs, taxonomy rules, and single-embedding models do not fully capture these properties.
In this paper, we propose LACRM (LLM-driven Advertiser Competition Relationship Mining), a novel framework that constructs directed competition graphs with continuous edge intensities via a three-stage pipeline.
LACRM moves beyond symmetric similarity through LLM-driven role-separated profiling, which enables asymmetric multi-route retrieval to uncover both realized and latent rivals.
The framework further incorporates listwise learning-to-rank aligned with temporal auction behavior to accurately model directed competition intensity.
In offline retrieval experiments on an industrial benchmark, LACRM outperforms strong directed GNN baselines and ablation variants.
Multi-LLM and human studies provide complementary evidence that the recovered latent rivals reflect meaningful market competition rather than merely model-generated artifacts.
In production A/B tests, LACRM-guided bidding and incentive allocation increase advertising revenue by 1.73% and 1.14%, respectively.
The results demonstrate the practical value of explicit competition modeling in large-scale advertising systems.