- Project Summary:
Project Idea: “Movie Matchmaker” aims to revolutionize the entertainment industry by creating a personalized movie and TV show recommendation service. Utilizing a sophisticated AI algorithm, this service will analyze users’ viewing histories across various streaming platforms and recommend new content based on mood, genre, and occasion. Sales on specific media and platforms will also be shown to provide the best user experience.
Vision Statement: To create a highly personalized viewing experience that caters to individual preferences and enhances content discoverability across streaming platforms. Think of it as the already existing AI on platforms like Netflix that give you recommendations based on what you’ve already watched and your ratings, but for all different streaming platforms, as well as possibly physical media.
Skills and Technologies: The project will leverage machine learning, natural language processing, and data analysis to develop a robust recommendation system. Skills in software development, AI modeling, and user interface design will be crucial.
- Needs Assessment:
Sector Analysis: In the entertainment industry, there’s a growing need for more sophisticated recommendation systems as the number of available streaming options and content continues to expand.
AI’s Role: AI can address these needs by implementing advanced pattern recognition to suggest content that matches users’ historical preferences and current mood, enhancing user satisfaction and platform engagement. Essentially, the AI will get to know you and your entertainment media preferences and will give you more content to watch based on its impressions.
- Skills and Technology Overview:
Required Technologies:
– Machine Learning (ML): For predictive analytics and recommendation algorithms.
– Natural Language Processing (NLP): To analyze user reviews and feedback.
– Database Management: To handle large volumes of user data and content metadata.
Skill Assessment:
– Current Skills: Basic programming knowledge, understanding of ML concepts.
– Development Needs: Advanced ML training, NLP techniques, full-stack development for app integration. Web designers and developers.
- Learning and Development Plan:
Learning Strategies:
– Enroll in specialized online courses in AI and ML (e.g., Coursera, Udacity).
– Participate in workshops and webinars focusing on AI applications in entertainment.
– Engage in project-based learning to apply skills in real-world scenarios.
– Continue to take college courses related to the use of AI and web development tools.
Budget:
– Online courses: $500
– Workshops: $300
– Materials and software: $200
- Application and Impact:
Application: The skills and technologies will be used to develop the Movie Matchmaker app, integrating with various streaming platforms to provide a seamless and personalized user experience. The applications and website should contain customizable elements like choosing the colors and style of a user’s interface so that it feels like a personalized tool and not just another app.
Impact:
– Beneficiaries: Individual users who seek more tailored entertainment options, as well as an experience that takes the work out of having to think or search for what to watch.
– Societal: Promotes a more engaging and satisfying entertainment consumption experience, potentially increasing cultural access and diversity in viewing habits.
- Project Timeline:
– Months 1-3: Complete educational courses and initial technology setup.
– Months 4-6: Development and testing of the AI algorithm.
– Months 7-9: App development and integration with streaming platforms.
– Month 10: Beta testing with selected users.
– Month 11-12: Full launch and marketing.
- Budget:
– Learning and Development: $1,000
– Software Subscriptions: $500 (APIs and server costs)
– Development and Marketing: $2,000
– Total: $3,500
- Evaluation and Reflection:
Evaluation Metrics:
– User adoption rates and retention metrics.
– Accuracy of the recommendation system (user satisfaction surveys).
– Feedback on usability and functionality of the app.
Reflection:
– Monthly review meetings to discuss progress, challenges, and adjustments needed.
– Post-launch, a comprehensive review to evaluate the overall success and areas for improvement.
– Easy in-app and on-site bug reporting and an entire team dedicated to responding to and fixing problems on these report tickets as they come.