Technology

Smart MedAI builds recommendation systems that are practical, readable, and grounded in user intent. The goal is to help people understand their taste while maintaining privacy and control.

Recommendation Systems

We combine behavioral, content, and contextual signals to create suggestions that feel personal and grounded in real preferences.

Behavioral signals What you watch, finish, skip, or save helps us understand preference direction.
Content signals Metadata, themes, and descriptors help build meaningful similarity beyond genre.
Context signals Intent, mood, and moment influence what feels right now.

Hybrid Models

Hybrid approaches capture both what people like and why they like it. We blend collaborative and content-based methods to keep recommendations accurate and interpretable.

Taste Modeling

Taste is an evolving profile, not a static list. We model change over time across media types to reflect nuance and long-term growth.

Long-term profile Stable preferences that should not be overwritten by short-term behavior.
Short-term intent Temporary signals that shape recommendations for a specific moment.

Explainable AI

Recommendations should be transparent. We design explanations that are short, honest, and grounded in real signals.

Privacy-first architecture

We minimize data collection and avoid advertising incentives. The focus is on useful personalization without invasive tracking.

Minimal collection Only capture what is needed to provide the experience.
No ad profiles We do not build or sell marketing identities.
User control Clear settings for data access, export, and deletion.

Evaluation

We evaluate models with a mix of quantitative accuracy and qualitative experience. The goal is to balance novelty, relevance, and clarity.

Future Research

Areas of focus include cross-domain taste transfer, privacy-preserving personalization, and interfaces that help people explore their own taste.