Earlier this month Microsoft and Facebook announced a new partnership that essentially makes Bing a social search engine. Danny Sullivan has written a great overview, but basically if you’re logged into Facebook and use Bing, you’ll now see results which are based on content viewed and shared by your Facebook friends: in this way, “Likes” start to gain the same value as links in the old search algorithm.

Bing & Facebook Together

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Now, I can’t comment on exactly how good this new system is due to the fact that like so many other beta launches, it’s not yet available outside of the US. However, to my ears, it sounds like an evolved version of Google’s existing social search product and, if it is, I’m not really sure it’s that exciting.

You see, whilst I have a lot of things in common with my friends (like an interest in me), I also have many friends with whom I share very little. For example, whilst a large percentage of my friends have young families and live in London, I live in Dublin and have no kids to worry about, so if I do a search for car insurance, seeing what products my friends have liked or recommended will almost certainly be of no use to someone who drives a car designed for hairdressers. What I really need is something that will tell me what people like me found useful, not what people I like found useful.

And this brings me, rather neatly, to Unsocial. According to Techcrunch, it’s like “Foursquare Plus LinkedIn Minus All Your Friends”:

A user signs up via his or her LinkedIn account and Unsocial uses that profile to collect basic information on the person’s career. The user inputs “smart tags,” or rather keywords, that describe his/her professional background (for example I might include “journalist,” “writer,” “technology”) and the traits of people s/he is seeking….[Unsocial] will show you relevant, nearby matches, if you find someone you want to connect with you can send a direct message through the platform (or e-mail/call them if that information is available). The service is ideal for large conferences or similar events where attendees have a limited amount of time to identify high-value contacts.

Whilst the app itself is far from perfect (due, again, to the fact that it’s limited to the US at the moment and also appears to extract very little from the LinkedIn profile other than name, meaning you have to type in all of your interests), it does hint at a much more useful future for (un)social search. Because if I was able to tie some sort of Unsocial-type application into my search engine of choice, my search for car insurance wouldn’t present me with options for people carriers, based on the current life-stages of my friends, but instead would show me insurance aimed at 30-something men without any kids.

Of course, if we want to find a search engine that does something like this, we don’t need to look to Techcrunch, instead we need only look at that most web 1.0 of sites: Amazon. For years its success has been based on making recommendations, something, it strikes me, the next generation of search engines will have to learn how to do.

If Amazon ever does tie up with one of the search giants, the people behind EPIC 2014/2015 (released in 2004 & updated in 2005) can look on contentedly whilst shouting “Told you so” at everyone they know.

A History Of Online Media

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Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Social | Search & Social

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About The Author: is the Head of Digital for Mindshare Ireland, as well as holding a global role for the media agency as Director, Emerging Media. At Mindshare he works with both local & multinational clients, helping them to integrate on & offline, and to utilise search, social, mobile & video in their broader marketing mix.

Connect with the author via: Email | Twitter | LinkedIn



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  • http://www.position2.com Vinod Nambiar

    Great article. I “like” the point you make about people “like me” and the people “i like’!

    But i would assume for most cases, stats will go on to prove that people you like..share a lot in common

    Vinod

  • http://www.iexposure.com C. Lobo – Internet Exposure

    This is an interesting point of view on the growth of social search. And one that I agree with, I think as our social networks become more and more populated with everyone we’ve ever met throughout our lives, unsocial search will be preferred. A new feature that allows us to optimize our search results based on only certain persons’ in our network’s “Likes” would be even better! Perhaps this technology is not far off the horizon.

  • grantmiller

    that is a really interesting spin on search… i love the idea of combining demographics as a higher level meta data to your personalized SERPs. In terms of social search, a few months ago i did a quick prototype of search results organized by the number of total facebook \likes\ for each url & launched it as proof of concept at: http://www.likelysearch.com … it is a simple implementation, but some of the results are pretty interesting for broader category type searches.

  • http://www.mindshareworld.com Ciarán Norris

    Thanks for the comments – I’m glad you all found it interesting.

    @Vinod – I agree that friends often share a lot of commonalities, but not everything. I think that what @Lobo suggests is the right way – somewhere in between, taking note of both. Interestingly, this would appear to be the way that Google sees the social web (I’ve tried to find a slideshare presentation where a Google developer expands on this, but I can’t – sorry!)

    @grantmiller – looks really interesting; a geographical slant would be a fascinating addition.

 

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