UX Research · Information Architecture · UI Design
Uneed – Smart Content Discovery App
Personalized content recommendations based on emotional context.

Goal: Transform an underperforming recommendation app into a personalized, engaging experience that users love returning to. The mission: make content discovery fast, relevant, and emotionally connected.
Challenge!
During early research, several critical issues emerged:
  • -1-
    No Clear Positioning
    Users didn’t understand what made the app unique or why they should return.
  • -2-
    Weak Personalization
    Recommendations felt generic and didn’t reflect user mood or preferences.
  • -3-
    High Cognitive Load
    Too many steps to get relevant content. No emotional context in the flow.
  • -4-
    Low Retention
    Users abandoned the app after 1–2 sessions because the experience felt “empty.”
Research & Key Insights
Main discovery:
Users want quick, mood-based recommendations without endless searching. They want suggestions that feel personal, not generic.
  • Analyzed reviews on App Store
  • Conducted 17 in-depth interviews with movie, series, and book lovers
  • Mapped pain points and behaviors
Summary table of user interviews
Point of View
These insights shaped our design hypotheses
  • How do we help Herman, a tired office worker, find a relaxing movie in seconds?

  • How do we help Irina, a film lover, learn and feel inspired by what she watches?
  • How do we help Peter pick the next audiobook without scrolling forever?
Hypothesis Testing
I created 4 prototypes, each exploring different approaches:
  1. Mood-based filters
  2. Quick swipe selection
  3. CJM-driven personalization flow
  4. Contextual recommendation cards
We tested these ideas to answer:
What makes content discovery feel effortless and engaging?
Final Design
  • Streamlined onboarding with interest tags
  • Mood-based selection integrated into the main flow
  • Visual recommendation cards with contextual hints
  • Reduced cognitive load through simplified UI patterns

Built a clear User Flow in Figma covering all scenarios: onboarding, filtering, empty states, and recommendations.

Impact
  • 87% of users preferred mood-driven discovery
  • Feedback highlights:
    “It feels like the app understands me.”
    “Finally, I don’t waste time deciding.”

  • Increased engagement by 63% during testing
Reflection
I turned a generic content app into a personalized discovery experience by focusing on emotions, speed, and simplicity.
When design speaks the user’s language, the product starts working.
design doesn't stop here