Enhancing Mood-Based Content Recommendations for Netflix

Duration: 4 Weeks

Platform: TV

Design tool: Figma, usability testing platforms, qualitative analytics

Role: end‑to‑end design—research, prototyping, and validation

 

Project Overview

In today's streaming landscape, users often want content that resonates with how they feel in the moment—not just what they've previously watched. My goal was to design a “Search by Mood” feature that matches suggestions to emotional dynamics, helping users swiftly find content that fits their current mindset.

Problem Statement

The current Netflix recommendation algorithm inadequately adapts to users' dynamic moods, leading to frustrations and reduced platform engagement. Users expressed dissatisfaction with repetitive suggestions and difficulty finding content that matched their emotional state during specific moments.

Research Objectives

The goals were to:

  1. Investigate how users discover content and what influences their exploration behavior.

  2. Analyze the emotional impact of viewing patterns on content preferences.

  3. Design and test a "Search by Mood" feature that leverages user input for real-time mood-based recommendations.

 
 

Methodology

User Interviews

  • Conducted 15-20 minute moderated interviews with six participants representing varied demographics and streaming behaviors.

Persona Development:

  • Created detailed personas to reflect user types such as "Film Enthusiast" and "Casual Viewer."

Prototype Testing::

  • Designed a prototype incorporating "Mood-Based Recommendations" and conducted usability tests to gather insights and refine the feature.

 
 

Proposed Solution

Search by Mood: A feature integrated into the Netflix interface allowing users to select or filter content based on emotions such as "Relaxing," "Exciting," or "Feel-Good." This solution enhances discoverability and aligns recommendations with users' current emotional states.

Search by Mood Prototype for Netflix UX Case Study

 

Design Highlights

The feature offers dynamic mood categories such as "Calm," "Energizing," and "Reflective," allowing users to quickly find content that fits their current emotional state. Recommendations adapt in real time, letting users update their mood mid-session for a more personalized experience. To enhance accessibility, the feature is available through both the home screen and the search menu.

 
 

Testing & Feedback

Users tested both guided and unguided scenarios, such as searching for relaxing content after work. Overall, 75% found the "Search by Mood" feature intuitive and engaging, while 50% noted that some mood categories were unclear and suggested improving the labeling for better clarity.

Impact

The feature improved user engagement by allowing faster content discovery based on emotional preferences. Prototype testing also confirmed increased satisfaction, as users appreciated the personalized and diverse recommendations tailored to their current moods.

 

Takeaway

Users strongly respond to content that matches their emotional state, making mood-based personalization essential. They prefer features that are easy to find, like mood filters and categories, and are frustrated by repetitive recommendations based only on past views. The "Search by Mood" prototype met user expectations, with 75% finding it intuitive. Clear mood labels and multiple access points improved usability, while real-time adaptation and larger screens enhanced engagement. Users also valued the ability to give direct feedback through mood tagging. Looking ahead, AI and contextual data like time or weather could make recommendations even more personalized.