Zemanta, a semantic service that extracts entities within the text of a publisher’s content and suggests related media, links and tags to add to a work as it’s being written, has launched a content discovery network to complement its suggested recommendations for which authors create original content.

The focus here is on providing editorial control. Publishers can feature content recommendations from their site, other web sites (Zemanta has 300,000 publishers in its network), and advertisers, taking advantage of the option to let Zemanta’s semantic algorithms automatically make those selections for them or to take the manual content selection route. Another option is to blacklist sites that they don’t consider appropriate content sources.

The opportunity the new network brings to publishers, says Zemanta co-founder and CTO Andraž Tori, is to increase reader engagement and revenue. “If the publisher participates in our traffic exchange network with other publishers, they get more unique visitors. And if they participate in the promoted content program they get revenue share,” he says. Payment takes place once $50+ is accumulated, but, Zemanta cautions, it’s important for publishers to keep their eye on increasing traffic to their sites and being a credible source, not just choosing promoted posts for the money.

Credibility is helped thanks to what Zemanta brings to the table. “By understanding content deeper than competition (building on five years of natural language processing and semantic experiences), we’re able to suggest more interesting content and increase click-through rates,” says Tori, a regular participant in The Semantic Web Blog’s Semantic Link Podcast. “So readers stay on the site longer.

But here pure semantic technologies aren’t enough – the engine has to learn also about what is the attractiveness of each piece of content to each user.” Its algorithm goes beyond pure relatedness, he explains, to gather data about what’s being clicked and it uses that to tailor further recommendations. “So it is natural language processing  plus semantics plus reader behavior equals recommendation,” he says.

The content discovery network, Zemanta says, includes features to accommodate the increasingly mobile-device loving population out there: Specific mobile phone and tablet native recommendation formats are optimized for faster load times and smaller display sizes of mobile web browsers.

“We’re seeing mobile on the rise. Long-tail publishers have about 20 percent mobile traffic, while many larger publishers are approaching or surpassing half,” says Tori. This means that ways to engage users specifically on mobile become much more important, starting with the design and user interface, where infinite scroll is gaining steam. That’s the process of pre-fetching content from a subsequent page and adding it to the user’s current page.

Says Tori, “Infinite scroll really changes how content gets written. And because of infinite scroll, algorithms have to basically provide ‘programming’ based on our interests and contextuality.”