Personality-Aware Entertainment

An AI-powered assistant that curates, analyzes, and re-sorts entertainment content based on your personality, preferences, and mood.

Comment 2

Personality-Aware Entertainment: Our Building Journey

Inspiration

It all began in our first team meeting after the hackathon theme was announced. Instead of jumping straight into coding, we opened up about the real issues we faced in our own entertainment lives.

Ahmed shared: "I have a perfectionism issue—I can’t start a new book or movie until I finish the previous one. But if the one I’m stuck on is terrible, it kills my motivation to explore anything new."

Abdelrahman added: "For me, it’s worse. My OCD pushes me to complete movies in one sitting, no matter how bad they are. I’ve wasted countless nights on content I didn’t even enjoy."

Ammar confessed: "I spend hours watching reviews just to decide if a movie is worth my time. Sometimes I never even watch it afterward."

We realized this isn’t just our problem—millions of people feel overwhelmed by entertainment choices, yet unsatisfied with the recommendations offered by algorithms that only consider genres or popularity.

We asked ourselves:

-What if recommendations weren’t just about ratings or trending content?

-What if they were about who you are and how you feel right now?

This was the spark that led us to combine AI with personality insights and design a smarter way to connect people to entertainment.

What It Does

Personality-Aware Entertainment is a Chrome extension and AI-powered system that personalizes recommendations based on:

  • Your MBTI personality type (e.g., INFP, ESTP, INTJ).
  • Your preferred genres (action, fantasy, comedy…).
  • Topics you dislike.
  • Your mood at the moment (Do you want to relax, laugh, think deeply, or just pass time?).
  • Your exclusions (Don’t like a specific actor, author, or studio? The AI will avoid suggesting their work).
  • And how much time you spend on entertainment websites—we even developed a timer that tracks your browsing habits to fine-tune suggestions dynamically.

For creators, our tool analyzes their content and predicts which audience segments will connect with it best, helping them target promotions and improve engagement.

We’ve also added a “Fit Checker” feature that appears next to each website or search result. This shows:

  • The entertainment type (e.g., drama, comedy, thriller).
  • The best-fit personality types for the content.
  • Whether it aligns with your preferences or not.

How We Built It

1. Designed a lightweight Chrome extension using HTML/CSS/JS.

2. Connected it to Gemini AI Studio for deep personality-based content analysis.

3. Built a re-sorting system that annotates search results with genre and personality tags.

4. Developed a browsing timer to measure how long users spend on different platforms, enhancing our AI’s understanding of their real preferences.

5. Focused on an onboarding flow to make the experience interactive and insightful from the start.

Challenges We Ran Into

1. Oversimplification of personality types: At first, our AI relied too heavily on MBTI categories. But people are more complex than 16 personality boxes. We solved this by layering in preferences, moods, and content filters.

2. Mismatched recommendations: Sometimes the AI perfectly matched a personality and genre, but testers still disliked the content. Why? Because the style of storytelling also mattered (e.g., fast-paced vs. slow, dialogue-heavy vs. visual). We added five targeted questions about narrative preferences to fix this.

3. Mood variability: People don’t always want the same content. Comedy fans might crave deep drama when reflecting. So we integrated a mood selector to make recommendations dynamic.

4. Personal exclusions: Some users didn’t want suggestions from certain directors, studios, or authors. A blacklist feature was developed to honor these preferences.

Accomplishments We’re Proud Of

  • Building our first AI-integrated Chrome extension.
  • Creating a truly personalized entertainment experience.
  • Designing tools for both consumers and creators.
  • Developing an innovative timer feature to make recommendations even smarter.

What We Learned

  • AI models aren’t magic—they require careful prompting and human insight to deliver real value.
  • Frontend UX matters as much as backend intelligence.
  • User feedback (even from family members during testing!) is crucial to refining the system.

What’s Next for Personality-Aware Entertainment

  • Expanding to mobile apps for on-the-go recommendations.
  • Allowing creators to test-market content with personality-matched beta audiences.
  • Generating AI-powered trailers and summaries tailored to individual users.
  • Training a self-learning ML model for even sharper recommendations.
Share this project:

Updates