Prismatic is looking to offer a personalized information content service which covers the user’s full range of interests.
These days there are plenty of mobile apps purporting to provide personalized online information content. Social networks such as Facebook and Twitter are also seeking to offer subscribers the latest news based on what they are interested in. However, their recommendations tend to be restricted to certain fields, comments already published on their sites, and to the user’s known social network activities. Now news aggregator Prismatic, a San Francisco-basedstartup, is looking to build a social network based on giving the user a really personalized newsfeed that matches the full range of his/her various interests. The company, whose services are based on machine learning algorithms, has just launched a new version of its iOS application, which adds a number of improvements to its recommendation and newsfeed system. Since it was founded last year, Prismatic has raised $15 million in financing from a number of investors, including venture and growth equity firm Accel Partners and the Russian investor Yuri Milner.
Newsfeed covering the user’s range of interests
The Prismatic service works in the same way as an RSS newsfeed such as Feedly or the now-defunct Google Reader, but is mainly fed in response to the stated interests of its users plus social network-related updates. The mobile app enables you to read the latest news, find out what’s going on in your social network and discover new topics relating to the activities of people you want to keep track of. You enter the topics that interest you and indicate the people you want to follow, and can then read articles, view the photos on offer and share content in response to given topics. As users share and comment on articles, the system learns more about each of them and becomes increasingly able to make more meaningful recommendations. Prismatic is essentially an iPad and iPhone app, and does not for the moment include such social network-type features as status messages or profile pages to maintain. Nevertheless, one of the founders’ aims is to facilitate discussion and interaction between users in order to increase their engagement with the platform.
Data analysis central to the Prismatic strategy
What is rather special about Prismatic is that the company has strong scientific data and machine learning expertise and, in contrast with its most of its competitors, is therefore more oriented towards big data analysis than pure media. The startup uses natural language processing, which enables it to develop an understanding of the key interests of its users and so offer content that matches those interests. The main advantage of Prismatic is its ability to provide a huge amount of varied news and information which is not initially known to its users. The site currently indexes five million new stories every day and contains over 10,000 topics you can follow. Company founder and CEO Bradford Cross argues that other approaches to information-sharing do not work so well as they are usually rigidly aligned with certain fields and therefore fail to take the broad picture of a user’s range of interests into account. In the coming year, the company is first of all planning a drive to widen its user base, and will then focus further on content, e.g. adding video. However, the personalized newsfeed segment is highly competitive and various types of players are getting interested in this market. For example Facebook has the ambition to become a fully personalized online newspaper, online social news magazine Flipboard, which aggregates web links from social circles, is also gaining in popularity, while N3twork, a new discussion application based on user interest, has just raised $12 million in funding. This type of social network, which sets out to offer a more representative picture of a user’s personality and all the things s/he is interested in, could open up a number of opportunities for commercial companies, including a new approach to segmenting their customers and finding out what really makes them tick.