
flipora
Flipora automatically learns what you like and recommends content that matches your interests. Get the iPhone app at https://t.co/E7UPr036qR.
Flipora emerged from Stanford University, founded by graduate students Jonathan Siddharth and Vijay Krishnan, to address the challenge of web content discovery. The company developed a platform that utilized artificial intelligence and machine learning to provide users with a personalized stream of content. By analyzing a user's web browsing history, social media activity, and in-app interactions, Flipora's system could infer interests across thousands of categories to deliver relevant articles, websites, and media.
The service functioned as a content discovery engine, moving beyond traditional search by anticipating user interests rather than waiting for explicit queries. Users could follow specific topics as well as other users with similar tastes, creating a multi-layered personalization engine. The platform incorporated a social component, allowing users to upvote content, which would then promote it to their followers, enhancing the discovery process within the community. Flipora provided its service for free and was accessible through its website and dedicated applications for both iPhone and Android devices.
The business model aimed to incorporate a smart advertising system, inspired by Google Adwords, to deliver highly targeted and relevant sponsored recommendations that would feel less like advertisements and more like useful suggestions. The company successfully raised capital, including a notable $1.5 million in 2015 from prominent investors such as Gokul Rajaram, the creator of Google AdSense, and executives with ties to Microsoft, Facebook, and Twitter. By April 2014, the platform had grown to 25 million users worldwide. In late 2015, Flipora rebranded to Rover, a name chosen to better reflect its mission of exploring the web to bring back valuable, undiscovered content for its users.
Keywords: content discovery, personalization engine, artificial intelligence, machine learning, recommendation service, web discovery, interest graph, social curation, mobile application, targeted advertising