In its announcement of Explore for the Web, Foursquare says its mission is "adding an 'interesting' layer to the whole world, tailored just for you." Foursquare Explore draws on the check-ins, tips, lists and interests of your friends to put a layer of "interesting" - which is apparently a noun at Foursquare - on a map. This is a challenge to Google Places and Maps, which is racing to add "interesting", but Foursquare's 1.5 billion check-ins give it a strong position.

When Foursquare recommendations launched in March, our Marshall Kirkpatrick called it Foursquare's Google moment. It was a Facebook moment, too; both Google and Facebook are trying desperately to get users to check in to places, so they can monetize the recommendations to users' friends.

Unlike the big kids on the playground, though, Foursquare is a mobile-first company, and that's where all its data comes from. Today's launch of Explore on the Web brings that wealth of information back to the desktop (and tablet).

Mobile-First Makes Desktop-Best

Foursquare has earned 15 million users so far. That's no Facebook. That's not even a Google+. But all the Foursquare users are there to check in and recommend places. That's a strong signal for a service like Explore.

This isn't the first desktop Web feature Foursquare has added lately. In November, the whole Foursquare website got a makeover, setting the stage for today's additional recommendation layer. That month, Foursquare also launched a save-to-Foursquare button for websites, allowing users to save places to their Foursquare to-do lists.

Google's Foursquare Moment

Google has been hurriedly adding these kinds of features, too. It acquired Zagat for a reservoir of professional place recommendations, and it's added lots of gee-whiz visual stuff to its desktop Maps interface.

In November, as Foursquare made its key desktop moves, Google started to turn the screws on Yelp, highlighting Google Places recommendations on Google Maps. But this is a very basic interface. It's not much of a threat to Yelp, let alone Foursquare, whose place recommendations are much more detailed.

Your (Fun) Homework Assignment

Reading about the features isn't as fun as actually exploring, so try Foursquare's homework assignment.

At foursquare.com/explore:

• Find a place to go to lunch today that you've never been before (hint: look for the 'I haven't been to yet' checkbox).
• Search for a nice spot to try out tomorrow night (try searching "fun," "romantic," or "Friday").
• Pick a city you've been wanting to visit (Chicago? Paris? Rio?) and look at our personally-tailored top picks for you there, based on your check-ins from your hometown.

Now try the same at maps.google.com. How did the experiences compare? Let us know in the comments.

Google announced this morning that it is taking the inclusion of social recommendations in its search results a step further with the introduction of the long-awaited Google +1. The new feature puts a "+1" icon next to each search result, allowing users to recommend certain results and websites directly from Google, rather than on Twitter or Facebook.

Google calls the feature "digital shorthand for 'this is pretty cool'," but we have to wonder - who are you telling and why?

Soon enough, writes Search Engine Land's Danny Sullivan, Google will offer stand-alone +1 buttons for websites to put right next to its Twitter and Facebook sharing buttons, which makes way more sense. Who wants to share a search result before they ever click on the link to begin with? And would you actually back out of the site simply to click the +1 to share? Sullivan also notes that everything you +1 will be completely public and part of your redesigned Google profile.

Google Goes Social: But Why?

For months now, we've been waiting to see what's next with Google's much-rumored social initiative. We first heard rumors of this effort early last summer, when the project was supposedly called "Google Me" and was surely the next "Facebook Killer." Since then, Google bought Slide for $182 million, saying that it would help make its services "socially aware," and publicly denounced the idea of a stand-alone social network to take on Facebook.

With today's announcement (and recent introductions of things like the Google Toolbar and redesigned user profiles), we see the first rumblings of Google's social effort and they again appear to be unfortunately misguided.

Let's look ahead to the future though - Google has released the button for inclusion on websites, you can see all these +1s on your Google profile, but again - what is your motivation for clicking +1? So your friends have a better search experience? Is this actually something you care about?

With last month's inclusion of social sharing cues in search results, Google had the right idea. Users already share on Twitter, Facebook and otherwise for a variety of reasons. But the main reason they share there is to share, not to recommend the validity of a link in case someone searches for something similar.

As LifeHacker founding editor and ExpertLabs project director Gina Trapani tweeted upon hearing the news, "I don't see myself curating search results much. By the time I know a result is good, I've left. What about you?"

Tech blogger Louis Gray argues that the +1 feature is for much more than just search results, pointing to the new +1 tab on Google profiles. "The +1 data is also closely tied to your Google Profile, which is a sibling to Google Buzz, which also has following and friend-like attributes," writes Gray.

Simply providing a place for these +1 links to be aggregated still doesn't give me a reason to click the button. I already have bookmarks. I already have ways to socially share with my friends. So what gives?

While we surely haven't heard the last of Google and its social effort, we have to wonder if we have another SearchWiki on our hands. Including social cues in search results seemed like a natural progression of search, but that kept the idea of intention out of the mix. People were already sharing using Twitter. This, on the other hand, brings the user's intention for sharing in the first place into the mix and confuses matters. What do you think? Will Google offer a compelling reason to click that +1 button or is Tweeting and Liking enough for you? Can your Google profile ever become central enough to your online identity that +1 will feel like a social experience?

What should you be doing next? A former Googler named Jyri Engeström, whose microblogging service Jaiku was cooler than Twitter, but was acquired into oblivion by Google three years ago, now thinks he can beat Google to the punch on that question. His new service Ditto launches today (iPhone app). It aims to use social suggestions, structured data and your existing Twitter and Facebook social graphs to recommend places and activities to fullfill your every desire.

I'm going to the Amtrak Station in downtown Portland this evening and I'd like to grab food nearby, for example. When I clicked the big, plush button for the activity type "Eat Out" I was then given the option to drill down into a sub category of sub-types of this activity: Asian, Breakfast, Fast Food, Mexican, etc. Had I said I wanted to exercise, a whole other set of options would be available (jogging, gym, soccer, etc.) and the activity types end up acting as constraints on the types of venues that pop up from the Foursquare database when I click to select a venue. (If I happen to be within 120 meters of that venue, I can choose to be automatically checked-in there on Foursquare, too.)

Once I declare my intention to eat near the train station, my friends offered suggestions where I should grab food. It was cool. My expression of interest could have been pushed to Twitter or Facebook, or it could remain within Ditto, where such expressions make the most sense in context. (People complain when you Tweet about what you ate for breakfast already, imagine Ditto messages pushed to Twitter about the mere fact that you intend to eat breakfast!) If I like a friend's suggestion, I can thank them and they will get a little trophy on their profile.

Bumps in the Road

There's something that strikes me as a little awkward about the service, initially. I don't think it's quite got the grammar of intention-check-ins down quite right yet. When I said I wanted to grab food, the app suggested places near me right now. I'm going downtown to the train station though - there's no way to change my location to that spot and search from there. I can just say, effectively, "I want to eat food at the train station." And then use the free text comment area to say "I don't really want to eat at the train station, I want to eat near the train station, where I will be later, who can recommend a good place near there?" At that point the structured part of the data and search feel like more trouble than they are worth. I may as well use Twitter. The exciting part about Ditto is its structured activity types and the way they are supposed to just let me click a few big buttons and have expressed my desire for something to do.

It's not clear if I'm supposed to post to Ditto after I leave home or when I've arrived someplace. I'm not sure if the venue database is supposed to tell me where exactly I should go or if my friends are. I'm not clear why I would use Ditto to tell my friends where I am if I am already there. Is Ditto still for suggestions? Is it still unique? I suspect that Ditto is going to be most useful when I'm wandering around in an urban environment, with available friends nearby, with time to kill and a general idea of the type of activity I want to engage in but without clarity on where I want to do it. That isn't very often, to be honest.

It could just be a new way to use the social Web and thus feel unfamiliar to me, but the relationships between place and venue, intention and real-time, activity and recommendation, Ditto and other social applications like Twitter and Foursquare are all going to take some time to get worked out better than they are right now, I think. There's certainly huge potential here though.

Structured activity stream data, especially if offered in a standardized format, could serve as a platform not just for recommendations but for analytics, targeting across platforms and many other types of innovative services of value to users, service providers and commercial organizations.

Big Ambitions

The combination of a pleasing interface with structured activity data as output could make all kinds of things possible in the future. Engeström says the things the service learns from the way its users interact with the first set of options will influence how future activity types and iterations are implemented.

Nearby movie listings are integrated already, but Engeström says that books, movies and music will all be offered soon as well, using services like Netflix and Spotify. "The idea is to discover something new," Engeström says. "Foursquare rewards you for going back to a place over and over again, we're the oposite of that. We're looking for generic discovery mechanisms that will make it easy to apply to anything. We think this could replace Google as a way to find things to consume." (Ditto would then monetize those consumption recommendations through affiliate fees or advertising.)

That's a very ambitious goal, but the design, focus on data, vision and pedigree behind Ditto are enough to help it gather some prominent backing to pursue that goal. The company is funded by hot startup backers Betaworks (Twitter, Tweetdeck, Tumblr and many more) and True Ventures (StockTwits, Urban Airship, GigaOM and more). Those are investors who focus on companies with a strong love of data - so don't think that this app is just about pretty pictures and sharing things with your friends.

Ditto is fresh out of the gate and will likely see a whole lot more features and interface development soon. The minds behind it are very sharp, and the opportunity here is huge. I hope the service ends up feeling like something I want to launch regularly.

I'm not sure I want Google telling me what to do next - but I think I would like it quite a bit if a service like Ditto, and my friends using it, could come up with some good ideas.

It's a development of historic significance: few events have changed the experience of life on earth as much as last century's proliferation of hundreds of millions of automobiles. The automobile was a revolution in personal autonomy, but it came with great costs. Now we've entered an era when that personal autonomy will become automated and some of the automobile's costs could be mitigated as a result. As a technologist, I find it helpful to understand the emergence of the fabled self-driving car as a convergence of three trends: the Internet of Things, Big Data and Real-Time Technology. Those trends are poised to go far beyond a self-driving car.

The Internet of Things

The personal automobile was invented near the end of the 19th century and popularized throughout the 20th - approximately 600 million now traverse a world of roads. There is 1 car for every 11 people on earth, but sometimes it's hard to remember that - they can seem so ubiquitous.

As widespread and significant as they are, though, automobiles themselves have been to date relatively unconnected to each other. They are big, dumb, fast-moving hunks of metal. Essential, in fact, to keep apart from one another as they hurtle down the highway.

"Your car should drive itself," Google CEO Eric Schmidt foreshadowed in a public statement last week, days before the Google self-driving car was unveiled. "It's amazing to me that we let humans drive cars. It's a bug that cars were invented before computers."

The instrumentation and networking of previously offline objects, like cars, buildings, roads and more, may represent the next big stage in the evolution of technology. A network-connected car in particular - primarily navigated by artificial intelligence able to leverage a world of data hosted in the cloud - promises big gains in safety, efficiency and quality of life for everyday drivers.

This product illustrates the potential for what's called the Internet of Things in a big way, but it should be understood in the context of the much larger trend. "Trying to determine the market size of the Internet of Things is like trying to calculate the market for plastics, circa 1940," says Georgetown University Communication, Culture & Technology professor Michael Nelson, the former director of Internet Technology at IBM, and the former director of Technology Policy with the Federal Communications Commission, in a February report by The Hammersmith Group. "At that time, it was difficult to imagine that plastics could be in every-thing. If you look at information processing in the same way, you begin to see the vast range of objects into which logic, processors, or actuators could be embedded."

Big Data

How is Google able to drive a car? In part because its software has access to an incredible amount of very detailed data: maps of the world, speed limit information, live video of a car's surroundings, sharp computer vision analysis of what that live video contains and big-picture data about an emerging network of cars in motion on the road.

Multiply that by not just one car, but countless cars throughout our roads and you've got a whole lot of data being processed.

Where there are network-connected devices, there are waves of data made available. Where there is big data, there are opportunities for pattern analysis, rational decisions and recommendations based on that data.

It's hard to know whether Google's self-driving cars ought to be called recommendation technology based on big data, or whether this is going beyond recommendation and into directive commands.

Big data is, in large part, about decision making. A Google self-driving car will make it easier to make decisions about the important parts of driving by automating the parts that don't require human decision-making.

As Michael E. Driscoll wrote this spring,

"...as information generating processes become more frictionless -- as humans have been excised from information read-write loops -- the velocity and volume of data in the world is increasing, and at an exponential rate...

"As the Big Data stack matures, tools that help manage the workflow from data to analytics to visualizations, and ultimately to decisions, will be critical. Someday, creating and sharing a data analysis through a web dashboard should be as easy as writing a blog post. Until that day, there's plenty of work to keep us data scientists well-employed."

Or managing the data through automobile navigation, as the case may be. It's not hard to imagine new, self-driving automobiles competing in the market based on their navigation algorithms or the ways they enable human drivers to relate to all the rich data made available by sensors and cameras on cars.

Real-Time Data

The real-time Web has been mistakenly characterized as nothing but a stream of Tweets and Facebook updates.

Just as real-time automated trading has changed the stock market forever, real-time delivery of big video and sensor data to and from the cloud and the car on street will change driving forever.

Accident avoidance is the most-obvious example, but there are others. Google spokespeople have talked about car-sharing programs that drive cars to the location of a would-be user on command, avoiding the inefficient use of valuable parking spaces in every neighborhood. Presumably those vehicles could predict where they are most likely to be needed, based on real-time data about existing behavior.

Ultimately, though, real-time will be most evident when you're in the car and it makes decisions based on surrounding circumstances as they unfold.

History

The point is, trends like the Internet of Things, big data and real-time data are poised to impact the human experience in many different ways. Their convergence in the sci-fi scenario of self-driving cars is the example that will be most evident to everyday people.

The consequences of such a technology on the psychology of autonomy, the ecology of transportation and urban planning and the million-plus lives lost every year due to human negligence behind the wheel, will be profound.

Even more profound, though, will be the spread of these same technology trends throughout our lives. Beyond our cars, automation and real-time analysis of data-rich environments could change the human experience in many, many ways in the relatively near future.

Schmidt says he believes that in the future, your mobile phone will quickly and automatically deliver personalized information to you based on your physical location and interests. "Since you are in location X right now, and have interest Y, Google thinks you'd like to know information Z," the search giant will effectively say to your phone.

"Ultimately, search is not just the web but literally all of your information - your email, the things you care about, with your permission - this is personal search, for you and only for you.

"The next step of search is doing this automatically. When I walk down the street, I want my smartphone to be doing searches constantly - 'did you know?', 'did you know?', 'did you know?', 'did you know?'.

This notion of autonomous search - to tell me things I didn't know but am probably interested in, is the next great stage - in my view - of search."

That sounds pretty interesting, as long as you can turn it off and exercise some control over what's being sent. "What's that ping notification you just received," your mother-in-law might ask as you travel through town together. "Oh nothing," you might reply, "just Google telling me there is a business establishment nearby related to some of my recent search queries."

Seriously though, my long-term mobile search dream is this: Dear phone, please tell me about the history, ownership, news coverage and other information about the building I am looking at in front of me. Make that automatic and ambient and I'm going to be one happy Google Mobile Search user.

Many industry-watchers have separated search and recommendation, saying that recommendation could in fact be bigger than search: It's the search you didn't even know you wanted to perform yet.

PaidContent's Robert Andrews raises two very interesting points in his coverage: "1) Android is already a considerable power hog without searches being performed at every footstep; 2) if Google can increase searches to this incredible frequency, can it also ramp up search advertising in lockstep?"

Very related: Google CEO Schmidt: "People Aren't Ready for the Technology Revolution".

The next SRI/CALO app to launch may be TrapIt, a news feed reading and recommendation service designed to act as a "cognitive prosthetic" to "adapt to unexpected events" in situations of "intense information overload". The US Navy has used the core technology TrapIt is based on to parse through huge quantities of information for what's most relevant. Soon you'll be able to use it to find the best news about your obscure interests, in the web's otherwise overwhelming ocean of Justin Bieber references. The spin-off company has lots of high-profile backing (including Li Ka-shing), will launch later this or early next year and we've got the first screenshots below.

TrapIt has raised two rounds of funding, one lead by Asia's most powerful businessman, Li Ka-shing. Ka-shing previously invested in Facebook, Spotify and SIRI.

I like My6Sense, but I'm excited to see if TrapIt can live up to its promise to serve both novice and power users. Most recommendation services struggle to accomplish both.

With a background story like this one, it's sure to be a product launch worth watching closely. You can sign up to request notification of the public beta on the company's website.

Log in with your Twitter or Facebook account, answer as few as 20 quick and addictive taste-evaluation questions, and Hunch will turn the front page of the site into a list of highly targeted personal recommendations of movies, books, magazines, computers, meals, vacation destinations and more. It's really impressive.

Hunch is well-known for its "Tell Hunch About Yourself" feature, where thousands of users have answered hundreds of questions each about themselves. All kinds of correlations can be drawn from comparing these responses. People who are comfortable with public drinking fountains, for example, are much more likely to say they'd be willing to risk their lives for a stranger, Hunch has found.

Now Hunch uses that data and more to "personalize the internet." It's a smart change in emphasis.

Where do the recommendations come from? As far as I can tell, the magazine recommendations for example reflect the magazines that people with similar taste as I have (about seemingly random things) and people I'm friends with on Twitter or Facebook have said they like when looking for a magazine.

If you've tried the company's experimental Twitter predictor game, which guesses how you'll answer some very personal questions, based on who you are friends with on Twitter, you've gotten a taste for just how effectively Hunch can look into your mind and soul.

With this relaunch of the home page, the company has cut to the chase and put its most successful features front and center. It's a smart move and one that will make Hunch far more comprehensible and immediately useful.

Co-founder Dixon wrote a blog post six weeks ago on his personal site about how helpful it can be for a startup to pivot. "Ask yourself: if you started over today, would you build the same product?" he wrote.

"If not, consider significant changes to what you are building...You aren't throwing away what you've learned or the good things you've built. You are keeping your strong leg grounded and adjusting your weak leg to move in a new direction."

Today, YouTube is getting into the music recommendation and discovery game with an update to its music page.

This latest offering has a similar feel and, in some ways, feels like getting a bit of MTV back from the 90s, when it actually played music. Unlike sites like Pandora or HypeMachine, the big advantage here is visual - welcome back to watching a streaming feed of music videos, not just listening to songs. The new page "showcases the most viewed music videos, special promotions, curated playlists, unsigned talent and gives you the ability to create on-the-fly mixes." It also lets you simply chose a genre and go, letting the site pick the music from there. According to the blog, we can also look forward to local music listings in the near future.

To visit YouTube's redesigned music page, go to www.youtube.com/music.

If this intersection of 3 key social software trends is someday exposed more fully to all 500 million Facebook users and more - the Facebook vs. Google battle could become a fight between Recommendation and Search. Facebook recommendations are in the sidebar for most users today, but they are so powerful that it's worth betting they'll be center stage in the future.

What other website do people tell as much about themselves as Facebook? What other website do people connect as directly with people they know in the physical, off-line world? Facebook's ability to recommend friends that you actually know when you create an account, based only on your email address, is pretty jaw dropping in and of itself. Facebook says the page recommendation is based on users similar to yourself, but these recommendations are surfaced before you fill out your profile information. Facebook is using some seriously magical secret sauce to figure out who your friends might be, then what you might like based on your shared demographics, before asking you anything more than your email, name and age. That's pretty amazing. Presumably they are pinging 3rd party email databases - but that would be an interesting story to dig into!

All these personal details and connections can be cross referenced to create a rich picture of who you are and what you might like. There have been a lot of behind-the-scenes user tracking and profiling technologies developed over recent years - but what can come close to a system people opt-into and tell all about themselves?

Likewise, Facebook has for years been paying very close attention to the click-through, commenting and update-hiding rates of publishers on its platform. If your application gets a good response from users, for example, it's allowed to push more updates out over time. If relatively few people click on your links, then applications see their rate of allowed updates lowered.

Organizations, "brands" and other publishers with Fan Pages that people subscribe to ("Like") have their click-through rates tracked similarly. On the surface at least, it's a pretty straight-forward relationship between user demographics, publishers you're most likely to be interested in and who get a lot of engagement from their current subscribers.

The end result is subscribers for publishers on the Facebook platform and subscriptions for users. RSS never caught on with the mainstream, but Facebook updates have. Subscription to syndicated updates from a potentially infinite variety of niche publishers has long been one of the dreams of the internet. This represents an important upgrade from Facebook's introduction of about 100 suggested Pages to Like in February.

The Google-Battling Power of Recommendations

That Facebook says these recommendations cannot be purchased and are entirely algorithmic is very important. That's an important nod towards the democratizing nature of the system. Another would be if the algorithm privileged some relevant and high-quality but long tail publishers - not just what's popular and successful among similar people. It's hard to believe there won't be some paid option some time in the future.

Recommendation-geeks have argued that recommendation may someday become bigger, more important and more lucrative than search. Recommendation is like a smarter, pre-emptive search before you even thought to search for anything. The richness of the data that this is based on inside Facebook is truly incredible. This could be how the battle between Facebook and Google plays out: as Recommendation vs. Search. User demographics vs. search personalization. Publisher engagement vs. Pagerank. Now what does Google have to offer against Facebook's key feature - the Newsfeed people opt-in to get subscriptions (and ads, basically) pushed in front of them, side by side with baby pictures and friend updates, into the indefinite future?

It's too bad this had to happen under a proprietary platform with privacy problems. These subscriptions people sign up for were turned irretrievably public in the Great Privacy Implosion of last December. The idea of irretrievably public subscriptions is comparable to a requirement that your library book check-out history be printed on paper and nailed to the front door of your house. It's crazy and anti-social. Then, at the last F8 Facebook developers' conference, the company changed this policy and allowed users to make their subscriptions private - though they still default to being public.

None the less, I'm not sure there's ever been a platform in history that knew so much about people, monitored publisher effectiveness so closely and made subscription so easy for so many people.

Get and discuss ReadWriteWeb updates on Facebook here.

Fans support their favorite artists and bands by purchasing badges. 100% of the money for the badges go to the artists, according to the company's PR representative. As the fan purchases badges and engages in recommendation actions, they earn "rank." That rank gives the fan a chance at "superfan" status and, according to the company, creates opportunities for interactions with the listener's favorite musicians.

The southern California-based company is led by Jason Schultz, of Ambistia business incubation lab.

Fanit launched in preview mode at last month's Coachella music festival in 2010. After this preview, reflecting user input, the interface was updated the game mechanics were refined and it has now relaunched.

Fanit fans can elect to follow other fans and get their list of promoted music and bands.

With its emphasis on recommendation versus in-site play (although there are samples you can listen to) it resembles another site we recently wrote about, Bee.tv. The implied dialogue between musician and fan as well as between fan and fan adds an extra dimension that other music recommendation services, like Pandora and Last.fm, lack.

Whether the implied becomes actual might make the difference between a good idea and a popular service.

"Bee.tv employs a proprietary algorithm that includes contextual and semantic analysis, collaborative filtering, and thematic push to deliver personalized TV, movie and video content recommendations."

You chime in on eight movies from Superbad to Casablanca. I wound up with The Bourne Supremacy (sure), Observe and Report (eh, probably not) and All Through the Night (never heard of it). It didn't blow my mind but it wasn't crazily out of the park either. Presumably, as I use the service, and rate more offerings, the engine will hone in on my weirdness and before you know it, voila! Kentucky Fried Movie and Cities in the Mist.

Recommendations are broken into TV, Web, Mobile and iPad. A recommendation filtering mechanism can monitor your preferences.

YouTube and most online video viewing sites have recommendation algorithms, but Bee.tv is a stand-alone site, with an emphasis on the recommendation process.
The site provides you with a place to purchase each of its recommendations that are for sale, but unlike Hulu, say, it does not seem to be a platform for free programming.

Bee.tv's partners include Apple, YouTube, Tribune Media, Amazon, broadcast and cable networks and various online content creators.

Get Glue recognizes when you're looking at a website about a musician, a book, a bottle of wine, a movie or many other types of stuff. Then it makes it easy to look up additional info about that item across other websites like Last.fm, YouTube, Wikipedia, etc. It does all of this unobtrusively, with social streams, recommendations and a game.

The newest version of the service recognizes links on search results and social networks, allowing you to invoke a handsome pop-up overlay on those pages as well. Check out the little grey "G" below. Hover over it and you'll get a pop-up filled with options for learning more quickly and easily, without leaving the page.

Privacy Concerns

Glue tracks the pages you're visiting, which is ok, but by default it exposes topics you look at on your public profile. You can turn that off and only expose the topics you interact with on Glue, like giving them a thumbs up or thumbs down. Yesterday I found an entry for a disgusting medical condition on my public Glue profile, because someone else (I swear) used my computer to look the condition up on Wikipedia. I wasn't very happy about that. I now have the setting to expose visits turned off, but the company could explain even that better.

Glue is smart enough that it ought to be able to tell when I'm looking at web content that involves health, sex, money or other touchy topics and ask me if I want to expose those visits. This is just another example of the running debate around passive tracking, over sharing, privacy settings and default social software design.

It's not hard to change this setting and once you do you then you'll probably be pretty happy. It's a shame it's an all-or-nothing thing, though. I'd be happy to expose my browsing history to friends if the types of topics above could be excluded.

Get Glue is pretty awesome and the company adds new features all the time. My profile on the site is here. Come friend me up and we can be nerds together. Especially if you like looking up trippy stuff on the internet.

Disclosure: Glue CEO Alex Iskold is a long-time friend of ReadWriteWeb and one of the nicest, smartest people in the industry. (Read his heart warming personal story here.) His product was also something I disliked using for years until recent updates, so I feel pretty objective about my perception of it. Alex has particularly good taste in books and can be found here on Glue.

Of the thousands of entries, Gavin Potter, a retired management consultant with no formal machine-learning training managed to rise to number 17 on the Netflix Prize Leaderboard. Potter writes, "The competition has trained several hundred, if not more, people how to properly implement machine learning algorithms on a real world, large scale dataset...This is, almost undoubtedly, the world's largest set of data on repeated decision making and it's ripe for analysis. The analysis may not win the competition, but it sure should provide some insights into the way that humans make decisions."

The public knowledge acquired from the process of producing these algorithms will not only affect Netflix's ability to suggest customer desires across its movie titles, but it will also form a baseline for other business systems. In addition to streaming entertainment providers, companies like Amazon and Pandora have worked hard to produce the best possible predictive technologies. If these company can tap into our unique tastes, they can suggest products and services we didn't even know we wanted. So a 10% improvement on recommendations can equate to a lucrative sales increase.

A second shorter term Netflix prize is expected in the near future. According to the New York Times' Steve Lohr, the Netflix Prize 2 will be concerned with "taste profiles" based on demographic and behavioral data.

Your favorite current affairs news blog, which couldn't survive on Viagra ads, is now charging subscriptions. Your e-tail site of choice keeps recommending country music, which you outgrew years ago. And your default social network's constant entreaties for donations finally annoy you so much that you do the unthinkable: switch to MySpace (at least it is supported by News Corp's old-media money).

This is too much. So, you pick up a copy of Portfolio magazine and browse the ads for financial products, reassured that at least this medium knows how to target its audience.

Anyway, this extreme scenario plainly won't happen, even if Congress has its way and regulates behavioral targeting in some fashion. The advertising lobby is far too strong to let policing efforts slingshot us back to Web 1.0.

But the battle continues. We'll likely see some form of Congressional hearings, with consumer advocacy groups and watchdog legislators like Congressman Ed Markey (D-MA) trying to sort out whether behavioral targeting companies are really just stealth spy networks. The ability of ad networks, social networking sites, ISPs, and Web publishers to capture user data without disclosing that they're doing so will remain under the microscope. Who knows? We may one day see a national "Do Not Target" registry.

Let's hope not.

The main argument for behavioral targeting is obvious: profitable CPM advertising, which allows advertisers to better reach buyers, supports free sites. Users, in turn, inhabit a less cluttered ad environment with far more relevant advertising.

But it goes deeper than that. Targeting, when it works well, creates a seamless relationship between you, a site's content and the ads on it. Consider magazines like Architectural Digest, Vogue and Travel + Leisure. If you're interested in design, fashion, or travel, you might ending up scanning the ads as much as the articles. Now, consider that the Web can deliver those ads in a far more segmented way (if you're traveling to Turin, you're likely interested in Piedmont wines).

In October 2007, Microsoft paid $240 million to power Facebook's ads, driven by private user data. While jaws dropped over the $15 billion valuation, Facebook has used the capital to quietly build an advertising environment that shows what behavioral targeting can do. You may be too busy browsing your friends' baby pictures to notice the ads (and that is a downside: social networks are not purchasing environments), but as an exercise, go to your personal page and click the "More ads" link. If you're a regular user, chances are it's getting ever closer to nailing who you are.

Rather than showing a succession of Netflix ads, Facebook now more closely resembles a neighborhood store, run by a knowledgeable proprietor who makes smart suggestions about what you might want. Yes, that creepy spy is really just Mr. Hooper. Seriously. The dozens of clerks at your local Barnes & Noble can't possibly remember what you bought a month ago. But Mr. Hooper? He knows.

Let's face it: Facebook's targeting has gotten much better because it uses your personal data without explicitly telling you. Were you given the opportunity to opt out, you probably would. It's almost reflexive for us now: "No, thanks. I don't have time to read the fine print and consider the implications."

So when the behavioralists are trotted before the House Subcommittee on Communications, Technology, and the Internet, their first order of business will be the basics: that, fundamentally, the Web is behavioral targeting. LastFM introduces you to people who like what you listen to; DoubleClick tracks your browsing history to display relevant ads; Amazon tells you a new Dylan album is available because, well, that's what it does best. Digital advertising is killing the magazine industry not because it is a cheap boorish alternative, but because it can simply embed a little Java tag in your cookie and follow you until it (or you) expires.

That is not a bad thing.

Yes, targeting has its downsides:

• Small sample sizes. Remember that gardening book you bought for your grandmother last year? Amazon sure does.
• Psychographic characteristics cannot be determined by browsing alone. Clicking on a Rolex ad doesn't mean you can afford a Mercedes; gawking at eye candy does not a luxury buyer make.
• Perhaps most critically, every behavioral targeting company has to toss out user data on a regular basis or else invest in endless storage. AudienceScience, for example, can track 2 billion events per day but purges data every 90 days.)

Congress should in fact be looking for ways to foster better targeting, not limit it. The more an ad network knows about you, the more likely it will be able to serve an relevant ad and improve your user experience. iTunes makes better recommendations than most for a simple reason: more often than not, it operates from a larger collection of data. You may frequent only one or two sites powered by 24/7 Real Media's Open AdStream, but if you've got an iPod, you've spent time building an iTunes database of, say, a few thousand songs, which directly feeds iTunes' Genius.

To see if Genius is better constructed than other recommendation platforms, create an iTunes database of only 10 songs (which could very well be the number of books you purchased from Amazon in the last year). Then ask Genius to recommend a playlist. You will almost certainly not get that eerie "How does it know?" feeling.

What's the solution? The digital lobby certainly has to do a better job of educating Congress on the benefits of targeting. And self-regulation is critical: NebuAd's clumsy efforts to track browsing histories directly through ISPs hit every wrong note possible for privacy advocates.

The solution, though, may be more technological than regulatory. Suppose ad networks, social networking sites, e-tailers and search engines were to share IP data. The overall sample size would shoot through the roof, and both advertisers and users would benefit from ad placement that is as spot on as iTunes' Genius. Some kind of universal cookie, perhaps, could let e-tailers notify ad networks that, despite your apsirational browsing of ArchiteturalDigest.com, you still shop at Ikea, and so that $40,000 George Nakashima coffee table really is not a good fit.

Suppose further that consumers could control this data. For example, once you returned from your European vacation, you could simply delete your old browsing history on travel, eliminating any more ads for hotels in the Mediterranean.

"Yeah right," you say, "Google would never share its users' search histories with e-tailers, much less other ad networks."

Well, it doesn't have to. Your browser already contains that information. Having this URL...

http://www.google.com/search?q=kindle

... means that you Googled "Kindle." And having this one...

http://www.amazon.com/Free-ebook/dp/B002DYJR4G

... means that you searched Amazon for the Kindle version of Chris Anderson's book Free. Solely by parsing URLs, your browser could even tell whether your Amazon session ended with a checkout.

For certain advertisers, that is valuable information. And if that personal data were kept safe, you probably wouldn't mind if targeting companies used it to display an ad for Free on ad networks.

But let's say you did mind. Let's say you preferred random Viagra and Disney ads. Your browser could resolve this, too. It already stores thousands of cookies for you—in fact, it stores all of the non-logged-in data for sites across the Web. Mostly, this is the Web pages you visit, user names and passwords, and other form data, and it's not easy to discern what cookies are storing what. But your ability to monitor and manage your cookies could be enhanced with a simple extension.

If this extension could communicate with third-party cookie-based systems through a standard protocol, you could set simple controls for how much and what kind of data to broadcast. "Okay, re-targeting bot. If you want to drop a Java tag on me and follow me around the Web, here are the ground rules."

The rise and fall of NebuAd illustrates a rather simple truth of behavioral targeting: the data is yours, not theirs. Yes, ISPs could track your browsing history universally, unlike individual ad networks, social networking sites and search engines. And shared data would enhance the user experience. But because the data is yours, controlling it should be in your hands, not theirs.

Which brings us to the real treasure trove: data you've entered on password-protected sites. Why is Facebook, for example, the only beneficiary of the personal data you enter on that social network? It knows you're an iPhone user, New York resident, Ivy Leaguer, travel lover, and Texas Hold 'Em player for one simple reason: you told it.

If communicating that data through a browser-based protocol, at your discretion, turns Viagra ads into iPhone app ads across the Internet, wouldn't that be something? We all understand the nature of free: someone has to pay. So, why not turn your browser into Mr. Hooper?

Guest author: Chris Kincade is co-founder of DesignBuggy.

Two year old wine social network Snooth announced today that it is now powering wine recommendations for the 25,000 editor tested recipes on Conde Nast's food site Epicurious. Snooth says this is just the first of a number of big sites that its custom algorithm will power recommendations on. That cassoulet? Snooth suggests you serve a Montevina Terra d'Oro Syrah 2002 ($15) with it. Nice.

Wine with food has got to be one of the most familiar kinds of recommendations offline, but the online recommendation technology industry is a fast growing one. The belief is that quality recommendations will serve as searches you never knew you wanted to perform - helping users navigate from one logical option to another, possibly making more purchases as a result and hopefully being better served by the websites they visit.

A food site with good wine recommendations sounds pretty tasty to me. Snooth says its recommendations are based on ingredients, cuisine and cooking method.

Here's how it works. First, the keywords are parsed out of a recipe, then they are run through an extensive food dictionary and a long decision tree is then followed. Is it a soup, is it a salad, what is the primary taste? Beef and nuts tastes mostly like beef; beef and liver tastes mostly like liver. How the ingredients are to be prepared is determined by their proximity to preparation words in the recipe. Recipes with expensive ingredients will see more expensive wine recommendations, inexpensive ingredients (lobster vs. shrimp, for example) will yield less expensive wine suggestions. Goodbye old one-liners about "if you're eating chicken!"

Nibbledish, Cookstr, Chow? All cool recipe sites but no wine recommendations, much less very sophisticated ones. It's easy to see how recommendations can provide a competitive advantage in a niche like this.