Research Paper

Core Innovation

This paper introduces RecHarness, which separates optimization into bandit-based direction selection and LLM-driven hypothesis generation, enhancing search stability and efficiency. It also incorporates a jump-basin mechanism to escape local optima, enabling sustained long-term exploration beyond prior LLM-based trial-and-error approaches.

Why It Matters

Manual tuning of recommender systems is time-consuming and resource-intensive, limiting innovation speed. RecHarness reduces human effort and experiment costs by automating model improvements, enabling scalable, continuous optimization. This drives better user engagement and monetization for platforms relying on personalized recommendations.

Potential Customers & Pain Points

• Online advertising platforms – Need to increase ad revenue and engagement
• E-commerce companies – Need to optimize product recommendations efficiently
• Streaming services – Need to improve content personalization with limited experimentation resources

Business Model

SaaS platform offering subscription-based access to automated recommender optimization tools with tiered pricing based on usage and scale; potential revenue share from performance improvements.

Competitive Landscape

• Google Recommendations AI
• Amazon Personalize
• Microsoft Azure Personalizer
• Coveo
• Dynamic Yield

Implementation Challenges

• Integration complexity with existing recommender pipelines
• Dependence on quality and diversity of historical validation data
• Potential instability in LLM-generated code requiring human oversight

Validation Strategy

• Conduct extended A/B tests on diverse recommendation tasks and datasets
• Partner with large-scale platforms for pilot deployments
• Measure key business metrics like revenue lift
• engagement
• and cost savings
• Iterate on model and interface based on user feedback and performance data