MovieFindr is a self-initiated product design project focused on solving a common user problem: decision paralysis when choosing what to watch across fragmented streaming platforms. I designed a personalized recommendation app that helps users quickly discover high-quality movies and TV shows by combining:

• Taste-based personalization
• Streaming availability filters
• Rotten Tomatoes quality indicators
• Fast decision-support UI patterns

The goal was to practice end-to-end product thinking while designing a feature set that delivers real everyday value.

My Role

• Product Designer (solo project)

Project Duration

• August 2022 – September 2022

Streaming users often experience:

• Endless scrolling across multiple apps
• Recommendations that don’t match their taste
• No easy way to filter by only their subscribed platforms
• Weak quality signals when browsing
• Decision fatigue from too many options

Even when content is abundant, confidence in choosing is low. So, I framed the core problem as: Users don’t just need more recommendations — they need better decision signals and faster filtering.

Primary Goal

• Design a recommendation experience that reduces time-to-decision.

Secondary Goals

• Improve recommendation relevance over time
• Centralize streaming availability
• Surface quality signals early
• Support multiple discovery paths (personalized + trending + top-rated)

Decision: Show Rotten Tomatoes score early
Tradeoff: Could bias discovery

Reason: Users already use ratings heavily — hiding them adds friction Decision: Filter by streaming services
Tradeoff: Smaller content pool
Reason: Increases immediate watchability Decision: Quiz + Ratings personalization combo
Tradeoff: More onboarding steps
Reason: Faster personalization accuracy