Joint LLM-Infused Embeddings for Explainable Financial Recommendations at Scale

Traditional recommendation systems such as collaborative filtering and market-signal models often fail to capture investor intent and rarely provide meaningful explanations. In this talk, I will present a financial recommender that jointly models investor behavioral signals (holdings, watchlists, interaction patterns) and market dependency structures using fine-tuned LLM-based embeddings in a shared representation space.

I will explain how this joint embedding approach improves relevance, diversity, and stability compared to behavior-only or market-only baselines, and how we evaluate these gains both offline and in production-oriented settings. The talk will also cover architectural decisions, representation learning trade-offs, and explainability strategies needed for real-world deployment.

If you are building ranking systems in high-stakes domains, this session will offer concrete patterns for combining foundation models with structured financial data in a scalable and interpretable way.