Frontend application handles onboarding, recommendation inputs, and result presentation.
In Progress2026Featured
SAVR - Context-Aware Dining Platform
A full-stack dining recommendation platform that uses context, preference signals, and explainable flows.
A full-stack dining recommendation platform that uses user preferences, budget, social context, and intent to produce more relevant restaurant and experience suggestions.
Problem
What this project solves
Restaurant discovery is often generic. It usually filters by location or cuisine, but ignores why someone is going out, who they are with, what mood they want, and what constraints actually matter.
Solution
How I approached it
SAVR treats the dining decision like a context-aware recommendation problem. Users can describe their night, build their night through guided choices, or use a surprise flow to receive explainable recommendations.
Architecture
System structure
Backend API separates auth, restaurants, experiences, and recommendation routes.
Database model supports user preferences, restaurant metadata, atmosphere tags, and future event signals.
Recommendation layer maps user context to venue attributes and explains why a result fits.
Decisions
Tradeoffs and outcomes
Recommendation input model
Tradeoff: A simple restaurant list would be faster to build, but it would not capture why someone is going out, who they are with, budget, dietary limits, or atmosphere.
Outcome: Structured the product around Describe Your Night, Build Your Night, and Surprise Me so recommendation results can be explained and extended.
Backend and frontend contract
Tradeoff: Recommendation features become fragile when UI filters and backend scoring logic evolve separately.
Outcome: Kept API routes, data fields, and result cards aligned around explicit preference signals, restaurant metadata, and explainable output.
Proof before polish
Tradeoff: Premium visuals can make a product look finished before the recommendation logic and data model are actually useful.
Outcome: Prioritized working flows, authentication, restaurant data, presets, APIs, and recommendation cards before final visual polish.
Proof
Evidence and impact
This is the flagship portfolio project because it combines product thinking, full-stack implementation, recommendation logic, onboarding, and real-world user experience design.
FastAPI backend route structure
React and TypeScript frontend architecture
Authentication and onboarding direction
Recommendation flows for Describe, Build, and Surprise modes
Roadmap
Next iteration
Expand the proof gallery with recommendation-result screenshots and an architecture diagram.
Add a live deployed demo once stable.
Add dish-level explanations and event-aware scoring.