LACRM: LLM-Guided Mining of Directed Advertiser Competition Relationships in E-Commerce Advertising

Abstract: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.

Machine Learning (cs.LG)

arXiv:2610.13959 [cs.LG]

(or arXiv:2610.13959v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.13959 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.