LEVEL 2: EARLY DEVELOPMENT
User Understanding
LLM-Driven Semantic Profiling for High-Stakes Personalization
LEVEL 2: EARLY DEVELOPMENT
LLM-Driven Semantic Profiling for High-Stakes Personalization
Traditional algorithmic matching is obsolete. The keyword-based systems dominating recruitment and the collaborative filtering engines powering dating apps suffer from fundamental semantic failures and bias amplification.
User Understanding represents a paradigm shift: a three-stage, LLM-driven framework comprising Semantic Profile Generation, Pairwise Trade-off Analysis, and Personalized Semantic Filtering. This is the state-of-the-art approach for high-stakes, nuanced matching across domains from precision medicine to legal technology.
Collaborative Filtering doesn't understand why users make choices—it only observes them. The result: implicit biases become systemic, deepening racial and physical homogeneity.
Algorithms optimized for engagement and revenue, not relationship success
99% of Fortune 500 companies use keyword-matching ATS systems that can't equate "Product Lead" with "Product Manager" or understand equivalent experiences.
Qualified candidates rejected due to rigid keyword matching
User Understanding is not a single score—it's a multi-dimensional latent vector across four key domains:
Big Five personality recognition from free-text
Multi-label emotion detection (Ekman/Plutchik)
Domain-specific intent classification
Author profiling from text patterns
From Early Development to Production: Testing the Viability of Hostable Ensemble Models
"Understanding" is not a single task but a complex, multi-dimensional construct requiring validated models across personality, emotion, intent, and profiling.
The resulting model must be "hostable"—strict adherence to computational budgets for memory, latency, and cost at inference time.
Question: Can we synthetically create a larger Big Five corpus by mapping the data-rich MBTI datasets to scientifically validated traits?
Question: What is the minimum quantization level (8-bit, 4-bit, NF4) that preserves semantic understanding quality while fitting within an 8-16 GB VRAM budget?
Question: Does a heterogeneous stacking ensemble with task-specific specialist models outperform a single multi-task transformer?
Question: Can we achieve <100ms latency for the full understanding vector while maintaining F1 scores >0.85 across all dimensions?
Question: Is fine-tuned, locally-deployed model accuracy competitive with third-party LLM APIs while ensuring data privacy and reducing long-term costs?
Question: Can audited, domain-specific fine-tuned models demonstrably reduce intersectional bias compared to general-purpose LLMs in high-stakes contexts?
Customized implementation of the 3rd-party LLM approach is available today for enterprise deployment.
Further experimentation for private user understanding models is pending market demand and partnership opportunities to validate production viability.
Deployed across multiple production applications and research demonstrations
A joy-focused todo list that understands its users for more effective scheduling, task automation, and ultimately joy. User Understanding powers personalized time management by adapting to personality traits, emotional states, and individual productivity patterns.
Proof-of-concept implementation demonstrating LLM-driven semantic matching as an alternative to collaborative filtering, addressing bias amplification and misaligned incentives.
A comprehensive technical and strategic analysis of the LLM-driven framework, including computational challenges, bias mitigation strategies, and the architectural shift from exhaustive comparison to hybrid retrieve-and-rerank systems.
Interested in next-level personalization for your platform? User Understanding technology is available for enterprise licensing and custom deployment.
Private-cloud or locally-deployed architecture with no external API dependencies
Domain-specific fine-tuning and algorithmic auditing for fairness
Optimized for latency, throughput, and cost-efficient inference