Jun 29, 2023
No image
Completed
AI-Powered Personalized Media Suggestion System
$10,000+
2-3 months
United States
2-5
Service categories
Service Lines
Web Development
Domain focus
Business Services
Challenge
The AI-based suggestion system is a revolutionary new way to personalize the media consumption experience for users. Our client approached us with the challenge of developing a system that goes beyond traditional methods of recommending content based on past views. They wanted a system that could take into account external factors such as time, day of the week, events in the user's calendar, and feedback from the user.
Our team rose to the challenge and developed a system that continuously gathers information from all of the user's Apple devices about the media they are currently consuming, including music, podcasts, films, and series.
The AI-based suggestion system is a revolutionary new way to personalize the media consumption experience for users. Our client approached us with the challenge of developing a system that goes beyond traditional methods of recommending content based on past views. They wanted a system that could take into account external factors such as time, day of the week, events in the user's calendar, and feedback from the user.
Our team rose to the challenge and developed a system that continuously gathers information from all of the user's Apple devices about the media they are currently consuming, including music, podcasts, films, and series.
Solution
The system then builds vectors based on this information and user preferences added during the initial setup, allowing for highly personalized and accurate content suggestions. Additionally, by comparing vectors of different users, the system can find matches and suggest content that people with similar tastes are likely to enjoy.
To achieve this, we used advanced data manipulation tools such as pandas and machine learning techniques like scikit-learn's cosine similarity to train the suggestion mechanism. We also leveraged the Pyatv library to facilitate communication with Apple devices. The system also incorporates user feedback, allowing it to adapt and improve over time.
The system then builds vectors based on this information and user preferences added during the initial setup, allowing for highly personalized and accurate content suggestions. Additionally, by comparing vectors of different users, the system can find matches and suggest content that people with similar tastes are likely to enjoy.
To achieve this, we used advanced data manipulation tools such as pandas and machine learning techniques like scikit-learn's cosine similarity to train the suggestion mechanism. We also leveraged the Pyatv library to facilitate communication with Apple devices. The system also incorporates user feedback, allowing it to adapt and improve over time.
Results
This AI-based suggestion system offers users a new and exciting way to discover and consume media, providing a truly personalized and dynamic experience. It can be integrated into any streaming or media platform and customized to meet any client's specific needs.
This AI-based suggestion system offers users a new and exciting way to discover and consume media, providing a truly personalized and dynamic experience. It can be integrated into any streaming or media platform and customized to meet any client's specific needs.