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Flipora Exceeds 25 Million Worldwide Users

SUNNYVALE, CA -- (Marketwired) -- 04/30/14 -- The popular website discovery company, Flipora, announces today that it has crossed the 25 million user mark and is rapidly approaching 30 million international users. Flipora is a Web based recommendation engine that suggests websites to users personalized to their interests. The recommendation engine uses machine learning to intelligently infer a user's interests based on their Web browsing history and Facebook activity. The Web is the world's largest knowledge base and Flipora's goal is to categorize the world's knowledge and recommend it to users based on their interests.

"This has been an exciting, record breaking year for us, and user-growth is only part of it," says Jonathan Siddharth, Flipora's Co-CEO. "Flipora's user base has been almost doubling every year. We have also recorded an all-time high in new user registrations, with over 100,000 new registrations in a single day. This is a huge milestone. This is also the year we made the Top 10 list of social media sites for sharing, alongside Facebook, Twitter, and LinkedIn."

Flipora's personalization and recommendation engine analyzes 100 million websites each day, roughly 25 times the size of Wikipedia. The service monetizes through the inclusion of ads, and has been profitable since late last year with revenues growing at a rapid pace. Flipora's Co-CEO, Vijay Krishnan, says, "We take a long-term view to building our company and product, which translates to a focus on having a scalable business model alongside a great product and user experience. We expect to end the year with over 50 million users and to continue scaling our rapidly growing revenues. It's exciting times ahead."

Over the last year, Flipora has seen an astounding increase in its mobile user base. Today, about 25% of worldwide and 50% of US new users sign in through their mobile devices. To satisfy the need of this growing population, the company projects to release its mobile app for iOS later this year. Given Flipora's strong international growth, translations in 11 new languages have been accomplished; German, Spanish, French, Hungarian, Indonesian, Italian, Portuguese, Vietnamese, Chinese, Japanese, and Korean. This will help Flipora gain a significant foothold in many new markets.

One of the unique aspects of Flipora's recommendation engine is its ability to do intelligent personalization automatically. The service uses state-of-the-art machine learning algorithms to automatically infer a user's topic preference, based on their web browsing history and Facebook data. Additionally, users can choose to follow not only topics, but other users as well. This personalization module, called FlipCat, can analyze a user's data and categorize it to build each user's customized profile of their interests. This helps the recommendation engine to suggest interesting websites on topics that users will love. An API for FlipCat that developers can request access to has launched and is now avaliable at Significant updates to Flipora's core recommendation engine are also planned for in the coming months. Flipora continues to expand and prosper on its mission to categorize the world's knowledge and recommend the best of the Web to users based on their interests. Signup at to follow your interests and follow other interesting users.

About The Company
Flipora is a Web based recommendation engine that recommends websites to users personalized to their interests. Flipora uses machine learning to intelligently infer a users interests based on their Web browsing history and Facebook activity. The Web is the world's largest knowledge base and our goal is to categorize the world's knowledge and recommend it to users based on their interests. Try it out at

Starting out of Stanford University,, now has nearly 30 million users and has raised Venture Capital from some of the best investors in Silicon Valley, with prior successes such as PayPal, Twitter, Skype, Tesla, Baidu, and Hotmail.

Flipora -- World's Knowledge At Your Fingertips:



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