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TV Show-Matchmaking Apps

The 'MightyTV' App Matches Users with Shows That Fit Their Interests

— April 14, 2016 — Tech
'MightyTV' is a new matchmaking app their pairs users with television shows instead of potential dates. Using Tinder's popular swiping interface, this app gives users a new tool when it comes to figuring out what to watch next.

MightyTV is a mobile app currently available for iOS. The app combines a Tinder-style interface with machine learning and personalization mechanisms to match users with content that aligns with their unique interests. The app works by showing users the posters for different movies and then giving them the opportunity to swipe right if they like the film or left if they don't. Information is also collected from the user's Facebook profile and subscription services such as Netlix and Hulu. The app then used all of this information to provide personalized movie recommendations.

The app serves as the perfect solution for consumers who are looking for more personalized movie and TV recommendations.
Trend Themes
1. TV Show-matchmaking Apps - Machine learning and AI can be used to personalize content and match users with shows that fit their interests
2. Tinder-style Interfaces for Personalization - The swiping interface can be used for more than matchmaking, it can be applied to recommendations and personalized content
3. Integrating Social Media and Subscription Services - Apps can collect information from multiple sources to provide even more personalized recommendations and content
Industry Implications
1. Media and Entertainment - Companies can use machine learning and AI to better personalize content and create more engaging experiences for users
2. Mobile Apps - There is a growing market for mobile apps that use AI and personalization mechanisms to match users with content and recommendations
3. Data and Analytics - Companies that can collect and analyze data from various sources, including social media and subscription services, have a valuable resource to provide personalized recommendations and content
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