Doordash x Foodmood
Food discovery adapting to mood, weather & everyday routine
FoodMood is a contextual recommendation feature for DoorDash that helps users discover food based on mood, weather and everyday routines, reducing decision fatigue.
Role
UX Research
Product Strategy
Product Design
Team
Independent Project
1 Mentor
Timeline
August 2025 - December 2025
Tools
Figma
Product Overview
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Current Scenario
Food delivery platforms are optimized for speed and convenience,
but food decisions are often emotional and contextual.
DoorDash has built a fast and convenient door to door delivery ecosystem that works especially well when users already know what they want to order.
But food decisions are often shaped by moods, cravings, weather, routines, and emotional energy. During these moments, users frequently open the app without knowing exactly what they want to eat, leading to endless browsing and repetitive comfort choices.
Opportunity
What if Doordash could feel you before it feeds you?
How might we transform food discovery from endless browsing into more context-aware recommendations?
The Weather Breather
“During rainy days I crave something warm and comforting, but on hot days I want something light and refreshing.”
The Comfort Seeker
“After a long day at work, I’m too tired to think about what to eat. I just want something comforting without overthinking it.”
The Familiar Fallback
“I open the app wanting something different, but after scrolling for too long, I usually end up ordering the same thing again.”
Primary Research
Research Systhesis
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Context constantly changes cravings (weather, time of day, routines, and emotional energy).
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Users think about food before restaurants.
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Decision fatigue often leads to familiar comfort choices
Competitive Analysis
Food discovery today is driven by restaurants and convenience,
not user context.
Product Strategy
FoodMood introduces contextual food discovery around moments - into DoorDash’s existing ecosystem.
01_Replace static cuisine shortcuts with adaptive contextual prompts
The homepage shifts from static cuisine shortcuts toward adaptive contextual moments shaped by mood, weather, routines, and evolving user behavior. Instead of generic browsing patterns, contextual prompts create a more personalized discovery experience based on what users are more likely craving in the moment, while continuously learning from their behavior over time.
Current app
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Proposed feature
02_Shift discovery from restaurants to contextual food recommendations
Instead of navigating through endless restaurant menus, users are introduced to contextual food recommendations that match the moment.
03_Lightweight personalization and direct 'add to cart'
Users can quickly refine recommendations through lightweight craving filters like warm, spicy, sweet, healthy, or refreshing, and directly add dishes to their cart without navigating through multiple restaurant menus.
04_Subtle adaptive UI moments through weather and
seasonal context
During specific weather or seasonal moments, the interface subtly adapts through contextual visuals and recommendations - pairing warm comfort foods with rainy evenings or refreshing picks with hot summer days to create a more intuitive, emotionally-aware, and satisfying discovery experience.
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Final Prototype
Helping users discover what feels right at that moment
Reflection
Designing within an existing product ecosystem requires balancing innovation with familiarity.
This project challenged me to think beyond creating a standalone concept and instead design a feature that could realistically integrate into an existing product ecosystem like DoorDash. It pushed me to carefully balance user behavior, business structure, and established interaction patterns while introducing a new contextual discovery experience that still felt familiar, scalable, and aligned with the platform’s core experience.
Let's Build
Together
© 2026 Shimona Roy