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Social-Filtered Search

Recently, there was a lot of discussion about running searches on Twitter, using authority as a filter. The idea is to reduce Twitter search results to only those with a minimum number of followers. The idea garnered plenty of discussion. From that discussion, I saw some perspectives that I liked:

Frederic Lardinois: I would love to have the option to see results from my own friends (or those who I have communicated with through @replies) bubble up to the top.

Jeremiah Owyang: Organizing Twitter Search by Authority is the wrong attribute. Instead, focus search by your OWN social connections. People you actually know score higher relevancy. http://www.loiclemeur.com/engl…

Robert Scoble: On both services you should see a bias of tweets made by people you’re actually following. Who you are following is a LOT more important than who is following you.

Those ideas make sense to me, because they reflect the way we seek out information. I do think there’s room for search results beyond only your friends. Here’s what I mean:

social-filtered-search

The idea above can best be described as follows:

I’ll take any quality level of search results for my close connections, but want only the most useful content from distant connections.

The logic behind this is that any quality “deficiencies” in content generated by my close connections can be made up for by reaching and having a conversation with them. That’s not something I’d do with more distant connections.

The chart above has two axes: strength of ties and usefulness signals. Let’s run through those.

Strength of Ties

Harvard professor Andrew McAfee blogged about the strength of ties back in 2007. With an eye toward employees inside companies, he segmented our connections as follows:

strong-weak-potential-ties-mcafee

The segmentation works inside companies, and it also applies in the personal world. For example, on FriendFeed, my Favorites List is akin to Strong Ties. The rest of the hundreds of people I follow are my Weak Ties. Friend-of-a-Friend entries I see are my Potential Ties. And of course there are a lot of people I never see. Those would be the “None” Ties.

The hardest part of this segmentation is that people aren’t likely to take the time to create and update their Strong Ties. Rather, Strong Ties should be tracked via implicit signals. Whose content do you click/rate/comment on/bookmark/share/etc.? Extend this out to email – who do you correspond with the most?

For example, I tried out the social search of Delver. It lets you load in your social networks, from places such as Facebook and FriendFeed, and uses content from those connections as your search index. Innovative idea. What happened though is that when I run a search, I get a deluge of content. My social networks are too big to make the service really useful.

Here’s where apps that handle a large percentage of my clicks and interactions will have an advantage. FriendFeed, with an extensive library of content from my connections, has this quality. Inside the enterprise, workers interact with a limited set of applications. The company’s IT department can set up tracking of interactions to identify implicit Strong Ties.

Bottom line: determining Strong Ties via implicit interactions is scalable and useful.

Signals of Usefulness

I’ve already described these in the paragraphs above:

  • Clicks
  • Ratings
  • Comments
  • Bookmarking
  • Sharing

Implicit data + explicit signals are the most powerful indication of usefulness.

Putting These into Place for Social-Filtered Search

When I say that I’d want to receive search results, even without many signals of usefulness, from my Strong Ties, here’s an example.

  1. I’m planning to run a marathon
  2. What marathon training plan should I use?
  3. I run a search for marathon training.
  4. I see a tweet from one of my Strong Ties: “Just started my marathon training this weekend. 4 miles FTW!”
  5. I @reply my Strong Tie, ask what training program he’s using.
  6. I now can leverage someone else’s work on this subject.

Of course, I’d want to see well-rated marathon training programs too, like Pete Pfitzinger’s Advanced Marathoning. I’d want to see the content from my distant/non-existent connections that had the highest signals of usefulness. Not unlike Google’s algorithm.

But the key here is that I’ll make up for any deficiencies in the utility of content for someone I’m close to by contacting them. A search on ‘marathon training‘ in Twitter shows a lot of results. But I’m not going to reach out to most of these folks, because I don’t know them. I only want those with whom I can have a conversation.

As I said, the ability to track both implicit and explicit activity is key to making this work. Facebook, FriendFeed, Twitter and Enterprise 2.0 all seem like good candidates for this type of search.

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22Social-Filtered+Search%22&who=everyone

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In Praise of Inertia: MyYahoo Still #1

Over at TechCrunch, they’ve got a post up discussing the top six personal homepages. #1? MyYahoo. MyYahoo has been around for quite a while. 6-7 years? It’s an oldie, but still a goodie. It’s my homepage.

There are others on the list. #2 iGoogle looms as the big scary challenger. Given Google’s success over the past several years in other arenas, it’s surprising they haven’t taken the #1 spot here as well.

It’s a testament to inertia. Not inertia in any negative sense, like laziness or user ignorance. Rather, inertia as a reflection of human sensibilities and value systems. Something called the “9X problem“.

Harvard professor John Gourville put forth the idea of the 9X problem. The gist of his thesis: “a mismatch of 9 to 1 between what innovators think consumers want and what consumers actually want.” The mathematical term “9X” actually does have a little math behind it. And that math is key to understanding the 9X issue.

First part of the 9X equation is based on our comfort with what we have. Things we already know, things that we have invested time in learning and using, have a high psychological value for us. They satisfy some need. We’ve learned their strengths, and live with their weaknesses. When we compare something new to something we already have, we tend to overweight the value of what we already have by 3X. It’s a little scary to give up what you know.

Second part of the 9X equation is based on our natural skepticism about claims made for new things. This probably resonates for most of us. I know I tend to dismiss most commercials and advertisements. This is a healthy trait of people – otherwise we’d all be getting duped left and right. But it also means that we underweight the value of features for something new by a factor of 3X.

Multiplied together, this gives us the 9X factor.

This is powerful stuff. It means new things really have to deliver healthy gains in benefits. Some examples of “9X masters” come to mind. Google was such a leap forward in search relative to its competitors: very relevant results, clean interface. Apple’s iPod just blew the doors off other music players in so many ways. Honda’s reliability and fuel efficiency were miles ahead of Detroit in the 1980s and 90s.

But there are plenty of other cases where good products failed to dislodge incumbents. Supposedly, many other search engines have attained search results parity with Google. I wouldn’t know…I still use Google exclusively. And so do many others.

So there’s the 9X problem. It’s actually a really interesting concept. If you’re doing a startup, can you do in it a way that does not force someone to give up an existing “thing” they like? That way, you only have to deal with the 3X new product benefits underweighting problem?

9X is really about inertia. MyYahoo fulfills a personalized homepage need: news, email, stocks, sports, weather, etc. iGoogle, Netvibes and others have really nifty options for their pages. But have they delivered 9X the value?

The key is to hook your users. Once you get them, it’s hard to lose them. On that note, how about adding my little blog to your RSS reader? You can remove it any time you want… ;-)

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