When a user clicks on the News heading in their LinkedIn toolbar, they now get a splash screen that outlines the different categories or channels of news they can subscribe to. There are some fairly obvious examples such as Economy, Entrepreneurship and Leadership, as well as broader categories such as Healthcare, Technology and Social Media — and a few somewhat more unusual channels too, like “Things I Carry” and “My Best Career Mistake.”

Once you pick your categories, the site shows you a redesigned LinkedIn Today page that looks very much like the front page of a magazine website: there is one larger story with a big image at the top, and then smaller stories by category. But the biggest difference between a traditional magazine and LinkedIn’s offering is that the stories on LinkedIn Today come from everywhere — hundreds of different sites and publications, from Wired to the New York Times. In other words, the site is acting more like a Flipboard-style aggregator, which probably isn’t surprising since it recently bought Flipboard competitor Pulse.

Whether or not this is just another attempt by LinkedIn to make the site more “sticky” and get users to spend more time there, it has the potential to become a real competitor to other news aggregators and providers. As I mentioned in a recent post about why it would make sense for LinkedIn to buy Pulse, one of the tools the site has going for it is an understanding of a user’s “interest graph” as it pertains to their business and/or professional life. That’s a valuable commodity.

Under former Fortune magazine editor Dan Roth — who talked about LinkedIn’s media ambitions on a panel at our recent paidContent Live conference in New York — the company has been expanding its reach for some time, including the launch of the Influencer program. That involves bringing in prominent personalities like Sir Richard Branson and giving them a place to host their writing, something that is similar to what Evan Williams is trying to do with his new company Medium (although it is focused more on literary content).

LinkedIn may not have created a “massive media empire” — as one rather breathless piece posted (on LinkedIn Today, of course) described it — but there is no question the site has media-related ambitions, and it is following through on them. And its ability to target specific users based on their interest graph gives it a potentially powerful weapon that other media entities lack.

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Gravity’s recommendation system is based on its interest graph technology, which we detailed last year. Here’s how I described it then:

[T]he gist is that humans first serve as guides for machine-learning algorithms by determining connections between terms within large data sets, then the algorithms take over to complete the job faster than humans ever could. When they’re done, the humans step in one more time to kill any bad connections between terms. The result is a system that can determine with high accuracy that a person tweeting about Vanessa Laine (Los Angeles Laker Kobe Bryant’s ex-wife), for example, is probably more interested in basketball than about Laine’s date of birth or other accurate but irrelevant information.

As new content streams into Gravity’s system, it’s analyzed and categorized in real time, then presented to users accordingly based on their interests and behavioral history.

How Gravity’s platform works

Graph processing and graph databases — which store and analyze data based on their relationship to one another — are critical to our onlines lives, powering everything from online recommendations to social search to knowledge discovery. Graph technologies are also the focal point of some impressive life sciences work from companies such as Syapse and Ayasdi, which will be presenting at Structure: Data in New York next month.

But publishers struggling to stand out on a noisy web might have the most to gain from graphs and personalization, generally. At our PaidContent Live conference (April 17 in New York), executives from Prismatic, Zite and Bluefin Labs will take the stage to talk about the importance of personalization for helping consumers filter through the deluge of content online so they can find what they really want. It’s arguable that the trick to keeping readers happy is knowing what they want to read — possibly better than they do themselves.

According to Gravity, its platform currently “delivers more than 25 million personalized content recommendations per day to more than 200 million users. Beta partners have reported click through rates two to three times above previous levels, return visitation increases of 300 percent and session length increases up to 40 percent.”

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As I explained in March when, Gravity has built a semantic-analysis engine that tries to gauge a site visitor’s interest by looking at more than the articles that person reads. Thanks to an expansive database of topics and a hybrid man-machine machine learning system that takes into account behavior as well as content, Gravity can determine other topics that might be of interest even if those connections aren’t visible to the naked eye. The result of this analysis is called an interest graph, which is like a social graph only that’s concerned with interests rather than people.

Currently, Gravity claims its total body of graph data exceeds 18 million megabytes, or 18 terabytes. The company says the new money will help it expand operations in the United States and even deploy its own content-marketing platform.

Of course, interest graphs are useful for more than just automatically presenting visitors with the news content they’re interested in. Gravity also has a product for advertisers to better target potential customers, and an analytics service so publishers can get in-depth visualizations of who’s reading their content and what content works better than other content.

However, the obvious elephant in the room when talking about interest graphs is privacy and how to collect and analyze user data without crossing any ethical guidelines. This will become even more of an issue as web platforms try to share data across services in order to create a more unified browsing experience in which interest graphs follow users around the web to inform personalization algorithms at every step. And as we’ll discuss later this month at our Structure: Europe conference in Amsterdam, some governments take user privacy much more seriously than others, which can make businesses based on that data a little trickier to operate.

Here’s Gravity Co-Founder and CTO Jim Benedetto, along with privacy attorney Ashlie Beringer, discussing the issue with me at our Structure: Data conference last March.

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Across increasing verticals, it’s never been easier to use apps and services to instantly find things that might match my interests.

But I’ve been thinking about those on-demand recommendations in the context of a conversation I had this week with TastemakerX founder and CEO Marc Ruxin about the different between interests and taste. (His startup, which launched in June, gives music fans a stock market-like game for discovering and learning about artists and he was at the GROW Conference this week in Vancouver to give a talk about the value of taste.)

Taste, he said, cuts to the core of a person’s evolving identity – his past, present and future. Interests can be more casual and transactional – stuff you might have, not the person that you are.  But taste implies a commitment of time and thought and, beyond defining who you are, it can inspire others.

“You have the taste that you’ve done the due diligence to have,” he said.

In advocating for taste, he argued that in connecting people and spreading awareness about movies, food, films and cultural trends, social networking has made it so that “everything, everywhere looks the same.”

Even though the Web has given us the tools to find like-minded people, its filters aren’t good enough for us to always find and communicate with them at scale, he said.  The Internet has the potential to connect people with the most esoteric taste – such as those who love funny pictures of cats (a la this) – but not all tribes that organize around culture can find and deeply connect with their cohorts online.

As a result, his bet is that people will increasingly opt for vertical-specific communities that, in a sense, celebrate, organize around and help people develop taste.

“It isn’t about good taste or bad taste,” he said. “But that you make your taste your own.”

I’m not sure I totally agree with his assessment about the extent of social networking’s homogenizing effect, but I do think there is something to be said for dipping below the surface of what happens to be most accessible to really seek out and figure out the content and communities that most authentically connect with who we are.

Especially with the overwhelming amount of information available, it’s much easier to listen to the music Apple says I might like and read the books Amazon suggests, instead of investing the time and energy to develop my own taste — and I often follow Apple and Amazon’s recommendations, happily. But I also feel, a little bit, like I’m cheating. Putting in the work to make good choices from a larger pool of options isn’t just a learning and discovery process about the greater category of content, it’s a learning and discovery process about ourselves.

The web has brought many more options to our fingertips, making the process of selecting the communities and content we want to spend our time with all the more difficult. And, in some contexts, time-saving data-driven and human-curated discovery tools are incredibly valuable. But in others, I’m starting to think, I’d rather lose time than taste.

Disclosure: True Ventures is an investor in TastemakerX and the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.

(Image via Kram78 via Shutterstock.)

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One could characterize BrainSpace as just another semantic search technology, PureDiscovery CEO Dave Copps told me, but they’d be wrong. Whereas many of those technologies rely on linking together pieces of data within specific indexes using industry-specific ontologies, BrainSpace doesn’t try to index data at all. It cares that documents contain similar words or concepts, but it also cares about a lot more.

It’s trying to create something more like a semantic brain. “We want to create an environment that understands people’s interests in a deep, deep, deep way,” he said. That requires learning new connections for each new company and situation.

In practice, Copps explained, BrainSpace currently acts as a sort of “purgatory” in between a user’s query and any number of separate indexes. BrainSpace reads the query, analyzes it against what it has learned about the data, and then pulls the relevant information. The technology already has proven itself in the world of archived content — it powers semantic search across more than 350 million documents for LexisNexis’s services and is rather popular in the world of legal e-discovery.

But PureDiscovery has its eyes on even bigger fish, which is why it calls itself the first “post-search company.”

Connecting the dots between content and people

What PureDiscovery really wants to do — and what it’s working on for some customers — is build interest graphs for every user within the company. Aside from determining relationships between documents, that also means determining relationships between people, and between people and documents. It also means mashing up social-graph information with interest-graph information in order to improve how results are ranked and displayed, Copps said.

That process is a play straight out of the Klout playbook. Every document has a level of social attention, Copps explained, manifested in data such as who’s accessing it, how they’re sharing it and with whom. For publishers, that might mean analyzing who’s tweeting a particular story, or for other businesses how something is being shared internally. Because BrainSpace knows who’s interested in what, perhaps even their level of expertise, it’s able to learn a lot about the nature of that document.

“The litmus test we’re going to give ourself is, we should be able to match a person with a document without doing search,” Copps said. “[We should be able to define what an object is] just by looking at who’s touching it and how often.”

Turning it loose on the web

There’s clear value for this type of technology as a replacement for traditional search, especially within enterprises with lots of disparate documents and people, but PureDiscovery wants to make discovering relevant content even easier. And it wants to do it for consumers, too. It’s building a reader application of sorts that will let users highlight a particular passage within a text, and then will point them to other content on that topic, or to people who write a lot about that issue in order to start conversations or collaboration.

“The idea is we’re going to build a social brain,” Copps said. It’s going to start by aggregating tweets containing links from the top 10,000 or so Twitter accounts, then will expand to even more tweets and even to other sources such as Quora. The connections BrainSpace makes between these pieces of content will form that brain. Then, Copps said, the application will take what you highlight in a post and “press it against the brain.” You tell it what you’re interested in by highlighting something, and it points you in the direction of further information.

Copps said he’s not too concerned about directly monetizing the consumer business because, if it succeeds, PureDiscovery will have created a very large, connected network of people and content. Companies will be able to access this information, and they’ll also be able to build their own brainspaces that connect their internal brains — the data and people within their four walls — with the greater social brain.

But don’t expect PureDiscovery to waste energy analyzing tweets about Britney Spears and building graphs that know who’s who and what’s what in her ecosystem. Although, Copps joked, “We could turn Pinterest into an incredible interest graph [by pulling information from photos, users and comments].”

Rather, it’s limiting its efforts and energy to more meaningful work. “We have big aspirations, we want to cure disease and things like that,” Copps said.

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Much as social graphs are maps of our social media connections that follow us across the web, interest graphs are maps of our interests. Some companies want them to follow us across the web, too, meaning that wherever we go, there we are. There’ll be no more need to search through news sites for the stories we want, or shopping sites for the products we want, because the site will know as soon as we hit its system who we are and what we like.

Whether you’re fascinated or appalled by the idea of interest graphs, here’s a taste of how they might work.

1. Figure out what you like

I recently discussed the idea of interest graphs with Gravity CTO Jim Benedetto, who described how his company determines visitors’ interests so its content-industry customers can deliver personalized experiences. Gravity does that by tracking a user’s activity across the Gravity network of sites to try and get a true sense of a user’s interests so it can feature that content automatically whenever that user visits a Gravity-powered site. An example on the Gravity Labs site explains the process of building a user graph in some detail.

Technologically, Gravity accomplishes this via a big-data engine that sits above expansive data sets such as Freebase and DBpedia and helps its system determine what that user meant when he clicked on a particular article or made a certain comment; what his actual interest is. Describing the system last week in a post about software patents, I called it “a system that can determine with high accuracy that a person tweeting about Vanessa Laine (Los Angeles Laker Kobe Bryant’s ex-wife), for example, is probably more interested in basketball than about Laine’s date of birth or other accurate but irrelevant information.”

Actually, though, Benedetto said that Twitter and Facebook aren’t as good for determining people’s actual interests (and Gravity uses them primarily to figure out what’s generally popular) as are clicks and comments on other sites. He said a high percentage of tweets are from people sharing information that’s important to them professionally, while Facebook is limited largely to sharing information among a collection of people with whom users are often quite close. In either case, he said, “There’s a lot of posturing [to impress others] … that results in incomplete data.”

At MySpace, where Benedetto was senior vice president of technology, he said his team often found people would join groups based more on their feeling toward the person inviting them than on an actual interest in the issue. We’ll sign up to save the whales not because we care so much about whales but because we don’t want to disappoint a friend.

2. Take it with you

“One day,” Benedetto told me, “people will be able to apply their interest graphs on every site across the Internet.”

It’s a compelling vision. Much as our social graphs influence our search results when we use Bing or Google, our interest graphs will influence what we see wherever we go on the web. If you like skiing, expect to see deals on skiing gear when you’re perusing ecommerce sites, or stories about winter sports on news sites. And expect to see this without having to log in, as it will be based on everything you’ve done across the entire web.

If you think about it, we all probably have several disparate interest graphs hanging around already, even if they’re not as advanced as what Gravity is building. The uproar around Google’s personalized search? Based on an interest graph. Amazon’s recommendation engine? Interest graph. Yahoo News? Interest graph.

The real trick will be in figuring how to connect these disparate graphs to build a single, portable account of who we are and what we’re interested in. Presumably, methods such as OAuth, which enable API-based data sharing among services, will have to play a role. Login once when you get online, and your interest graph is ready to go, ready to make the web a place that serves you rather than a place you must learn to navigate.

3. Rein it in

Of course, for every person in love with the idea of a personalized web-surfing experience, there’s probably one person who considers it a privacy nightmare. There likely are others who want an objective experience not tainted by any system’s assumption about what they want to see. For these reasons, the personalized web will almost certainly have to be an opt-in experience.

Privacy could be a particularly difficult problem to solve. Discussing the issue of the personalized web in a November report for GigaOM Pro (subscription required), I explained the necessity of empowering consumers to decide what data is a part of their digital fingerprint, or interest graph. Most people probably don’t want their affinity for pornography affecting the news they’re shown, or to be reminded of an impending foreclosure everywhere they go just because they’ve done a lot of research on the subject. Site permissions and “do not track” buttons might have to get increasingly granular, and increasingy easy to understand, to ensure the personalized web doesn’t become an enemy.

A true picture of our interests is one thing, but we’ll always need boundaries, especially as our lives get even more connected. Do you want ads for Valtrex popping up while watching Google TV with your new girlfriend? Do you want her popping on your computer to check the news and see wedding planning stories front and center, ruining your romantic proposal? Just as we can have our own rooms in our homes, or can drive across town and shop anonymously, we need pockets of privacy in the personalized web.

If you find the possibilities and pros and cons of a personalized web as fascinating as I do, be sure to get to our Structure:Data conference next week in New York, or at least watch the livestream. We’ll be talking all about the latest techniques for analyzing web and mobile data, and I’ll be speaking with Gravity’s Benedetto and Gibson Dunn partner Ashlie Berlinger about the line between what’s possible with analytics and what’s ethical, or even legal.

Interest graph image courtesy of Gravity; sherpa image courtesy of Ian S.

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