Well that next big thing is here (in a limited, private beta). Meet Hunch.

What’s Hunch? As Fake explained in a blog post:

Hunch is a decision-making site, customized for you. Which means Hunch gets to know you, then asks you 10 questions about a topic (usually fewer!), and provides a result — a Hunch, if you will. It gives you results it wouldn’t give other people.

Hunch is a personalized recommendation engine. But rather than, say, track your purchase history the way Amazon does, Hunch uses a combination of your answers and community feedback. The wisdom of crowds gets coupled with your own quirks to help you make decisions.

Let’s say you’re wondering “What’s the best laptop for me?”

A screen shot of Hunch.com

Hunch.com takes you through a wizard that will, by the end, give you an answer to your question that’s based on your answers. In my case, 3 of my 4 laptop recommendations were Macs because I expressed a preference for the Mac platform, and was willing to spend > $1000.

If the answer Hunch gave you was off — say Hunch suggested a paid text editor, but you wanted a free one — you can offer feedback and help correct the system. Users can also suggest alternative answers.

If you want to really up the relevance of the answers you receive, you can tell Hunch all about yourself by answering a series of questions.

And that’s where things get a little weird and slightly creepy. Each correction you add and every question you answer not only makes the system smarter overall, but — much like your Amazon.com purchase history — reveals a little bit more about you.

The advantage is that the more Hunch knows about you, the better your results should be. But the trade-off is that The Machine, and the Hunch community may end up knowing more about you than you are comfortable revealing. It aggregates yo’ sh*t man. Over time, a profile begins to emerge. Throw in community components like followers and that’s just a whole lot of data about who you are and what you might be about.

Quite frankly, it’s a marketers wet dream and I’m quite curious to find out whether — or, more likely, when and how — Hunch will capitalize on this data. That said, I don’t think Hunch is any more of a privacy or data threat than Friendfeed, Facebook, MySpace, Amazon, or my beloved Twitter. But I would like to see Hunch give users the ability to opt-out of being followed altogether.

I would also like to see weighted questions. Perhaps after you answer each question, but before your final answer is revealed, you have the opportunity to weight how important each factor is to the decision. I’m sure that will require some massive code-fu. But it would also improve recommendations.

Conclusion

I’m not sure why people would use Hunch.com to make decisions instead of doing what we do in real life: ask friends, go with our gut, use Google. I like the idea of a trainable recommendation engine that learns what I want and also enhances the community’s knowledge. I’m just not entirely sure it works as a standalone product, and I’m not sure how much community you can form around it.

Maybe that’s not the goal of Hunch, though. After all, Netflix posted a $1 million prize to anyone who can help improve its Cinematch product. Amazon uses recommendations to boost sales. Recommendations are potentially a big business. It wouldn’t surprise me to learn that Hunch is an experiment in building this kind of trainable technology rather than a product unto itself.

Related elsewhere

• Rethinking Recommendation Engines

If you’re not listening from the CBC‘s Search Engine podcast, shame on you. It’s a kick ass show that looks at technology and culture, globally, but with an emphasis on Canada and the United States.

Episode 25, “CCTVs, biometrics, and self-destructing data,” is particularly good. It’s an interview with Canada’s information and privacy commissioner, Ann Cavoukian about online privacy and using a combination of technology, law, and ethics to protect citizen and customer data.

What stood out for me was this point made by Cavoukian (at about the 6:30 mark):

Your ability to control the information you have shared with others and their subsequent control of that — that’s when everything falls apart. You may be able to restrict the information to five people. But what you can’t do is — you have no ability to control what they do with your information. And that’s when things get weakened in terms of the weakest link of the chain, in terms of security.

I have run into this a few times with my Twitter friends. I have a private feed, largely to keep it from being Googled. I understand that “private” online is not really private — and despite my propensity to overshare, there are some things I just won’t tweet. Yet I am still uncomfortable when items in my feed are retweeted, or captured in a screen shot and reposted. My feed is protected, dang it! If I wanted my content to be Googled or re-contextualized, I would have blogged it or published to my public stream.

As J.Brotherlove pointed out in a (protected) Twitter posting, however:

I see your tweet re: your issue with SNS. this isn’t new though. we’ve had this issue with email.

I’ll argue, though, that email (and instant messaging for that matter) are conceived of as a one-to-one conversation, rather than a one-to-many conversation. It’s true that you can copy-and-paste an IM or email into another medium. But our behavioral norm is that these messages should not be re-shared without explicit permission. It’s the same from a technical standpoint, yes, but not a social one.

When it comes to tweets, Facebook data, or Flickr photos, however? As a community, we clearly have different expectations for privacy on social network sites. As thatblackchick put it (in a protected Tweet):

The nature of social networking means that it’s a one-to-many conversation, therefore, the sharing rules are different (IMO)

And what’s compounding the problem? Jason says:

Twitter, however, and it’s tools, don’t value or promote privacy. I can’t tell by glancing at tweetdeck which people in my stream are private.

He isn’t entirely right. Twhirl differentiates between protected and unprotected feeds. Twitter does too, though design changes would make those differences clearer. [Jason corrects himself in the comments: "Twitter does value privacy. The lock icon is clearly seen on the site, protected tweets don't show up in RSS feeds, you can't deeplink to protected tweets and make them accessible to non-permissioned users." I DO think that some UI changes would make it clearer, however.]

The obvious answer to all of this, of course, is don’t tweet / post / publish that which you do not wish to be reshared. Still, I’d like a higher degree of informational self-determination and awareness on all sides.

I think Cavoukian and data privacy researchers are on to something with this idea that rules for data use are embedded in and travel with the data. Going forward, I would like to see tools, APIs and user interfaces that enable and encourage people to respect public/protected settings, or enable users to opt-out of certain privacy-eroding features.

Embedding data rules in Twitter posts (or Facebook pages, Flickr photos, etc.) may be technologically unfeasible if not overkill. But how about allowing users to make their posts unavailable to the API? How about interfaces (for desktop clients and the web) that alert users they are about to retweet an item from a protected twitter feed? How about letting users opt out of being retweeted? How about ultra-granular, per-tweet privacy controls (a la Plurk)?

None of those is perfect, of course, but they can encourage a culture in which people are more conscious of and respectful of other users’ controls.

Related here:

• What I’m reading: Privacy, security, pervasive technology and society
• Ethics, friendships and Flickr (or “Why I don’t drink around some folks”)
• Plurk.com: “Karma” and Community
• My Love Life. Online.
• On Twitter-ing
• Who do you trust more: Corporations or Government?

Related elsewhere

• “Social Media is Here to Stay… Now What?” by danah boyd
• “Getting Smarter About Twitter” by Jason Toney
• “What is YOUR use for Twitter” at MrTweet
• Podcast #26: how Spybots can fire you, and is “SmartData” personal DRM?
• “Privacy by Design” by some smart Canadians
• “How I use Twitter” by jbrotherlove
• “Blogging With Split Personalities” by Sarah Dopp

One of Plurk’s signature features is “Karma,” a rewards system that awards or removes points based on how users interact with the Plurk community.

Plurking daily builds karma. Taking a few hours or a few days away can lower karma. Having plurk-arrhea lowers karma. Invite your friends and your karma goes up. Get de-friended, and your karma goes down. Get your friend request rejected and you lose some more.

Karma, then, is a system that rewards users who practice good community behaviors — being active, but not obnoxious — and penalizes those who don’t.

But the weighting of user activity to calculate Karma has concerned or annoyed some Plurk users. Disappearing karma points = a disincentive to using the site.

I suspect users feel that way because karma is something that you earn. To lose it because of a busy day at work or a web-free vacation feels unfair. Couple that with a lack of transparency — which, much to its credit, Plurk has addressed — and you have a system in which some users opt out of the system of rules because they don’t know which rules they are being penalized for breaking.

I am still intrigued, though, by Plurk’s idea of rewarding or penalizing user behavior algorithmically rather than by community reporting or internal policing. One obvious advantage is that you mitigate some dumb mob effects.

On the other hand, your site runs risk of being perceived as a digital disciplinarian. You’re not the boss of me, dang it! Screw you and the karma-removing-horse you rode in on. I’m taking my ball and going home … or back to Twitter full time.

I think a better karma-style behavior system would:

1. Be transparent about the rules, norms, and expected behaviors.
2. Be very, very obvious about where users can find those rules, norms, and expected behaviors.
3. Always reward for use. Stay neutral on a lack of use. Deduct points only for more serious violations such as obvious spamming, or maybe obvious friend-whoring.

Elsewhere

• Plurk’s “Safe Plurking” Guide
• Plurking Help the Complete Plurk How To Guide for Plurkers old and Plurks Noobs too