Founder
TasteBud
Personalized Recommendations from Noisy Dining Data
Built a conversational food recommendation experience that turns messy menu and place data into trustworthy, context-aware suggestions users can depend on.
Highlights
Hyper-personalized
Recommendations tuned to mood, taste preferences, and budget instead of generic ranking lists.
Constraint-aware
Supports dietary and allergy needs including gluten-free, diabetic, and other food constraints.
Context-sensitive
Handles situational requests like date-night patios, kid-friendly dinners, and dog-friendly spots.
Background
Context
TasteBud reduces decision fatigue by turning broad food and drink discovery into highly specific recommendations tailored to each person in the moment.
Stakeholders
- Foodies looking for better local recommendations
- Travelers in new places needing quick confidence
- People with dietary or allergy constraints
- Groups with situational needs (family, pets, events)
Challenges
- Traditional review and map products return broad lists that often miss personal context.
- Users need recommendations that account for diet, allergies, budget, and location at once.
- Decision support should work across both discovery and menu-level choices.
- Recommendation quality depends on trust, freshness, and perceived reliability of data.
Objectives
Personalized Discovery
Deliver recommendations matched to taste, mood, and budget instead of generic popularity.
Constraint Coverage
Support dietary and allergy-aware recommendations users can actually act on.
Conversational Utility
Make the product useful for natural-language questions from broad to highly specific.
Research and Discovery
Use-case Taxonomy
Product scope was structured around three primary query categories: quick recommendations, specific constraints, and menu intelligence.
Preference Dimensions
- Mood and craving intent
- Dietary and allergy constraints
- Location and situational context
- Reliability and review quality expectations
Sample Prompt Modeling
Prompts were designed to cover common scenarios like date nights, work-friendly coffee spots, vegan options, and dish recommendations at specific chains.
Design Approach
Persona-led Voice
Framed the product as a trusted foodie friend to make interaction feel human and low-friction.
Prompt-first UX
Centered interactions around natural-language requests instead of rigid filter-only experiences.
Progressive Specificity
Supported both broad "where should I go?" flows and precise dish-level decision support.
Trust Signals
Emphasized reliable, highly rated recommendations and real-time data quality cues.
Solution
Recommendation Layer
Delivers highly rated restaurant and bar recommendations aligned to personal preferences and budget.
Constraint Intelligence
Handles allergies, dietary rules, and lifestyle filters without forcing users through complex setup.
Scenario Fit
Supports context-heavy requests such as group lunches, date nights, pet-friendly outings, and family dining.
Quick Recommendations
Examples: nearby sushi, work-friendly coffee shops, romantic dinner spots.
Specific Needs
Examples: vegan patios, kid-friendly gluten-free options, late-night delivery.
Menu Intelligence
Examples: what to order at a chain, spicy options, popular menu items.
Reflections
TasteBud validates a direction I use across my work: translate complex data and intent signals into approachable product workflows people can trust in high-frequency decisions.