Appropriately named for the Japanese expression that means "just what I wanted," Kita Kore is a social media website that recommends parties, concerts and other events to registered users based on their preferences and past engagements.
After supplying some rudimentary information including your age, willingness to pay for entrance and general interests, you're ready to party: Kita Kore will then recommend local events for you to attend. If you're into industrial metal, you could be directed to a free concert. Should you show interest in expensive whiskeys, you'll be informed about a nearby tasting event. And if anime is your thing, Kita Kore will let you know about a local convention.
Who knows, in three months you might find yourself in the throes of an exhilarating mosh pit at a minimal techno concert, thinking "Kita Kore!"
Intuitive Social Networks
Kita Kore is an Events Website That Learns From Users' Preferences
Trend Themes
1. Intuitive Social Networks - Kita Kore's event recommendation system shows the potential for social media platforms to use user preferences and past engagements to personalize content.
2. Personalized Event Recommendations - Kita Kore's algorithm-driven approach demonstrates the opportunity for event websites to offer tailored event suggestions based on user preferences and interests.
3. Data-driven Event Discovery - Kita Kore's use of user-provided information showcases the potential for event platforms to leverage data to enhance event discovery and attendee experiences.
Industry Implications
1. Social Media - Kita Kore's intuitive event recommendation system highlights the potential for social media platforms to integrate event suggestions and enhance user engagement.
2. Event Planning - Kita Kore's personalized event recommendations indicate opportunities for event planning companies to leverage user preferences and interests to curate tailored event experiences.
3. Data Analytics - Kita Kore's data-driven approach presents opportunities for data analytics companies to develop algorithms that analyze user preferences and provide valuable event insights.