We love decorating our office, so every once in a while we come up with ideas on how we can make our walls a little more interesting. The latest one took on a crowdsourcing vibe when we decided it would be cool to have everyone fill a photo frame with a person that inspires them in their work, and put them all up on one of our walls. Here’s the result:  

We at News360 have been working on news personalization for a long, long time now. The idea of tracking user interests based on reading behaviour, and using that data to recommend articles, sources and other things is very appealing – especially if you already have a lot of the basic building blocks with named entity extraction.   The obvious, naive idea is to just build a classification system.

Forbes published an article a couple of days ago detailing some inside information they have on The Daily’s numbers – writer Jeff Bercovici says he has been told that they have had 500,000 downloads and out of these, 75,000 converted to regular users. I thought it might be interesting to do a bit of analysis on these numbers to understand how good or bad they actually are.

There was a big row today as a set of high-profile news publications (including the Washington Post, AP, Time and so on) sent a cease-and-desist letter to Zite, a news-personalization iPad app (the letter is detailed in paidContent and AllThingsD). The cause? Zite was taking the original content (often, the entire article) and reflowing it inside their own interface, stripping out all the ads, internal links and so on. To be honest, once I saw the way content was being presented in Zite back when it launched, I was sure that this was going soon, and here’s why:  

The team here at News360 has a lot of knowledge to share with the world – we’ve been doing semantic analysis in news media for the past 7 years – first, in Russian, and now, since about a year ago when we started News360, in English. The ride of the past year has been wild – almost from scratch, we’ve created a semantic analysis engine, and started aggregating more than 1,000 different news sources, extracting more than 700,000 different named entities (companies, people, locations, brands and so on) from every article and released three mobile apps on the iPhone, iPad […]