Former Twitter engineers are building Particle.news, a news reader which entered into private beta, TechCrunch reports. The startup claims to offer a personalized, “multi-perspective” news reading experience that not only leverages AI to summarize the news, but also aims to do so in a way that fairly compensates authors and publishers.
The startup was founded last year by former Senior Director of Product Management at Twitter, Sara Beykpour, who worked on products like Twitter Blue, Twitter Video, and conversations, and who spearheaded the experimental app, twttr. Her co-founder is a former senior engineer at both Twitter and Tesla, Marcel Molina, the TechCrunch adds.
The company says, Particle Media, Inc.(PMI in short) is intended to redefine the way users consume web contents. It is a technology-driven interest-oriented content portal, “Interest Portal” in short, which facilitates users to efficiently define and explore their interests, evolves users’ interest by continuously learning from their interactions with the contents as well as the social circles, and algorithmically delivers the most personally relevant contents with respect to users’ interests.
“At the heart of PMI’s product lies our patent-protected “Interest Engine” which seamlessly integrates the cutting-edge technologies of search and recommendation to enable a unique and personalized content consumption experience per user. PMI uses its enriched user interest profile as “queries” to its deeply analyzed content database, which is in contrast with editorially predefined categories as queries for traditional web portals, user inputted keywords for search engines, ambiguous noisy long user “profile” for personalized recommendation engines, or social connections for social networks,” it explains.
Here’s how it works.
Particle News Reader 1) crawls, analyzes and indexes hundreds of millions of news articles from tens of thousands of high-quality news, blogs, forums, as well as major social media sites; 2) develops user interest profiles by learning from all kinds of user behavior data; and 3) delivers personalized news articles accordingly to its PC web and mobile users.
“Once the product concept is validated for the news domain, we will systematically extend it to other verticals and eventually the entire web,” PMI states on its LinkedIn page.
Compared with existing readers, Particle News Reader allows users to self-define any interests at their will, or subscribe interests out of numerous algorithmically generated and recommended polymorphic interests in a rich format of social tags, categories, topics, keywords, or news sources at any granularity from any perspective.
“It thus well satisfies users’ entire interest spectrum especially long-tail interest and highly informative deep contents instead of popular interest and entertainment driven shallow contents only. Particle News Reader makes news reading a rewarding experience rather than time-killing.”
The company states, “The more one interacts with our product, the better Particle News Reader understands him, and thus the more personally relevant contents will be delivered per user’s interest.
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