LACRM Framework Released: Revolutionizing Advertiser Competition Relationship Mining in E-commerce Advertising

Summary:arXiv has released a research paper introducing LACRM (LLM-driven Advertiser Competition Relationship Mining), a novel framework for mining directed competition relationships among advertisers in e-commerce. The framework constructs directed competition graphs with continuous edge intensities through a three-stage pipeline, leveraging LLM for role-driven asymmetric multi-route retrieval to uncover both realized and latent rivals. Experiments demonstrate that LACRM outperforms existing directed G

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1. Core Breakthroughs

The LACRM (LLM-driven Advertiser Competition Relationship Mining) framework addresses the challenge of modeling competition relationships among advertisers in e-commerce advertising. The key innovations include:

• LLM-driven Role Profiling: Leveraging Large Language Models (LLMs) to perform role-based profiling of advertisers, capturing the asymmetric and multi-dimensional nature of their competition relationships.
• Directed Competition Graph Construction: Building directed competition graphs with continuous edge intensities to accurately quantify the competition intensity between advertisers.
• Temporal Auction Behavior Alignment: Integrating listwise learning-to-rank methods with temporal auction behavior data to capture the dynamic changes in competition relationships.

2. Technical Mechanism Analysis

LACRM employs a three-stage pipeline:

1. Role Profiling: Utilizing LLMs to perform in-depth role-based profiling of advertisers, generating preliminary descriptions of their behavioral characteristics and competition relationships.
2. Competition Relationship Mining: Employing multi-route retrieval methods to uncover potential competition relationships between advertisers and constructing directed competition graphs.
3. Competition Intensity Modeling: Combining temporal auction behavior data with listwise learning-to-rank methods to quantify the competition intensity between advertisers.

3. Engineering Trade-offs and Performance

• Strengths: LACRM effectively captures the complex competition relationships among advertisers, significantly enhancing the optimization decision-making capabilities of advertising platforms. In industrial benchmark tests, LACRM outperforms existing directed GNN methods.
• Challenges: The introduction of LLMs brings high computational costs, requiring a trade-off between model size and inference speed. Additionally, the dynamic nature of competition relationships demands robust real-time updating capabilities.

In production A/B tests, LACRM-guided bidding and incentive allocation strategies increased advertising revenue by 1.73% and 1.14%, respectively.

4. Developer Implementation and Deployment Recommendations

• Application Scenarios: LACRM is suitable for e-commerce advertising platforms, social media advertising networks, and other scenarios where accurate modeling of advertiser competition relationships is required.
• Deployment Recommendations: It is recommended to deploy LACRM in environments with strong computational resources and combine it with real-time data stream processing technology to achieve dynamic updates of competition relationships.
• Optimization Directions: Future research could explore integrating LACRM with reinforcement learning to further enhance the optimization decision-making capabilities of advertising platforms.

5. Conclusion

The LACRM framework provides a new technical path for modeling competition relationships in e-commerce advertising, demonstrating the great potential of LLMs in complex relationship mining. Its excellent performance in industrial benchmarks and production environments proves its value in practical applications.

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Source: ArXiv Machine Learning (cs.LG) (2026-10-14)

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Tags: #LACRM Follow #E-commerce Advertising Follow #LLMs & Foundation Models Follow #Competition Relationship Modeling Follow #Advertising Revenue Optimization Follow