|By Marketwired .||
|August 14, 2014 05:00 AM EDT||
SUNNYVALE, CA -- (Marketwired) -- 08/14/14 -- How much time do you waste browsing through uninteresting content on the Web or mundane content shared on social networks? Most content you see on social networks is not really related to your specific interests, but is content your friends chose to share. Wouldn't you like to use a service that is primarily focused on you and your interests? That would be Flipora, a free service that automatically tries to understand a user's interests and tastes and shows great content recommendations from all over the Web.
Flipora understands a user's interests by analyzing their Facebook and Web browsing history data. Once this service learns a user's interests, it automatically makes the user follow a great set of relevant interests and other users who are influencers in those topics. The data for this recommendation engine comes from the aggregate Web browsing history and attention data of the tens of millions of users already using the service. In effect, the content recommendations for you are based on what other like-minded users have enjoyed. This service is mood-aware in that it is able to infer automatically what interests are currently top of mind for you to make highly contextual website recommendations. In much the same way that Pandora recommends music to you based on music you've enjoyed in the past, Amazon suggests products you might be interested in buying based on your shopping history and Netflix for movies, Flipora offers a similar machine learning based recommendation engine that operates for the World Wide Web by analyzing a user's Web browsing history passively. By using sophisticated machine learning algorithms and the power of big data, this service has built one of the world's first Web scale discovery engines. Discovery is fundamentally different from Search in that, search works when a user already knows what they are looking for and can articulate it with a search query. Discovery, however, is about opening the eyes of the user to something new that they never knew existed and would have never known to search for.
Given its fast growth to 30 million users, it's clear that Flipora has tapped a real user need, which is effective discovery of content online. Interest based discovery, facilitated by the service, appears to be part of larger trend among consumer internet companies with the rise of services like Pinterest, Quora, Pandora, among many others, and it is great to have a service doing this for the World Wide Web. These interest based networks fulfill a need currently not served by general purpose social networks which are primarily about connecting with friends and maintaining real world relationships. Mobile apps for iOS and Android are also in the development stages right now for this incredible service. Once users have the browser extension (add-on) installed, they are offered highly contextual content recommendations, which are related to the current page being browsed. In many ways, the service provided is analogous to an intelligent tour guide who is extremely knowledgeable about your likes, dislikes and current mood and knows where to guide you next.
With over 3000 interests for users to choose from, the service lets one indulge every niche interest one might have. The great thing, however, is that the service automatically updates the set of interests one follows so that it is always current, reflecting one's current passions. Up-voting and sharing content found through this service is also an available feature which promotes the content to one's followers. It is also a terrific source of traffic for quality web publishers who create great content.
The service has really taken off in recent times with more than half of the 30 million users signing up in the last 18 months. The era of intelligent personalization has begun and the discovery of great content on the Web has never been easier.
Flipora is a mood-aware Web discovery service that uses machine learning to automatically learn your interests from your web browsing history and Facebook activity and then recommends new content based on your current interests. Being mood-aware, it is able to automatically infer its user's current interests, to make highly contextual content recommendations from all over the Web. In much the same way that Pandora recommends music based on music the user has enjoyed in the past, Amazon suggests products to buy based on shopping history and Netflix for movies, Flipora offers a similar machine learning based recommendation engine that operates for the World Wide Web by analyzing a user's web browsing history passively.
Starting out of Stanford University, Flipora.com now has nearly 30 million users and has raised $3.9M in Venture Capital till date from several top angel investors in Silicon Valley.
For more information about Flipora, visit: http://www.fliporareviews.com/
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