Google’s Social Search is doing the walk, all the rest are just doing the talk. As soon as I activated the Social Search experiment, my next search yielded a social result. No setting up, showing how I am connected to that result (including friends of friends), showing as part of the standard web results…

Contrast this with Microsoft’s poor attempt at “social search” by indexing tweets and status messages and showing them regardless of the actual searcher (example search, you’ve got to be on “United States” locale on bing to see it).

Then also contrast it with Facebook’s announcement back in August of its implementation of searching within friends’ posts - a less grandiose announcement that yet delivered far more social experience than Bing’s. Nevertheless, it’s a very limited experience and far from being a true information source for any serious search need.

So how does Google overcome the main obstacle – collecting your connections?

Google relies on its own sources and on open sources it can obtain by crawling the social graph. That is the true reason why Facebook is not part of Google’s graph (no XFN/FOAF marking on Facebook’s public pages). Google may be counting on Facebook’s inevitable opening up, and with Gmail’s rising popularity it becomes a reasonable alternative even for Facebook users like me.

Sadly, all this great news gave zero credit to Delver, where it all happened first…

By Roi Carthy, Techcrunch.com

Social search engine Delver, which we placed on death watch a month and a half ago has been acquired by Sears in a last minute play right out of left field.

Israeli business media is reporting that as part of the deal, Delver CEO Liad Agmon will move to Chicago where he will hold a title of VP at Sears Holdings. Delver itself will become an R&D center for Sears and will continue to develop its social graph search engine, as well as additional products. It is not clear what Sears wants to do with Delver. Perhaps it will turn it into a social product search engine, or maybe it just likes the idea of buying an Israeli R&D team on the cheap.

The purchase price is unknown but it’s safe to assume it could not be very high considering the company was literally days from being shut down. The bright side of course is that Delver’s remaining 20 employees will not join the unemployed in Israel.

The company has raised $4 million from a single investor, Carmel Ventures.

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:

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:

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

I had the pleasure today of presenting Delver in a talk I gave at IBM Haifa Research Labs IR  seminar. My slides are over here.

The seminar’s focus this year was on social search, and there were quite a few other talks I found very interesting, I’ll blog about those later on too. One of the positive surprises for me was the amount of work carried out at IBM-HRL on social/web 2.0 tools such as SONAR. Impressive social product work for a non-consumer player; I plan to read more of their published work on that.

As a followup on Brynn’s review of Delver, I’ve had an interesting exchange with Lachlan Hardy, where Lachlan expressed his disapproval of Delver’s crawling and unifying the social graph (content alone seems ok). My response is in this thread.

The important issue is that socially-connected search requires a comprehensive and unified social graph, which can be quite difficult to achieve. When users conduct their first search, they would expect all of their friends, friends of friends, and their respective content to be pre-indexed, for such a service to be of any use.

Skipping that part makes it impossible to bootstrap, and would be like a web search engine that includes only websites that opted-in to be included in the index, or like a FriendFeed version that shows no public profiles, and if you want to follow someone you must create their consolidated profile yourself. These can be regarded as far more privacy-observing services, but will probably never bootstrap as their real-life counterparts did. It’s all about keeping the balance right.

When I first started using gmail, I was shocked: “What? no folders??…” I couldn’t figure out those funny labels, and searching my emails instead seemed a strange idea. Nowadays, when I have to locate an old email, I pray that it’s on gmail and not in my Outlook (even with Vista’s improved search).

The dilemma between search and browse paradigms runs through many software user interfaces, and was especially emphasized with Google’s focus on search in their products. In some areas, such as finding web sites, the search paradigm has undisputably won and the once-king Yahoo! Directory barely has a stub article in Wikipedia. In others, such as news, search is a rarely used service, and a portal-like browse interface rules.

But in reality these are complementary paradigms, rather than competing. Browsing is excellent when the data fits a clear and sufficiently granular taxonomy, shared by the author and reader, and unstructured searching fits into all the other cases (and in some cases, like web search, that’s all there is). Oh, and one more difference: search is A LOT easier. Just stuff all the text into strong index machines, and give the user the ubiquitous search box.

With gmail I wouldn’t think twice before moving an email to the archive, I have no doubt I’ll find it when needed, and all the hassle of managing folders is gone. A blog is no different. You have an author communicating a heap of knowledge to readers, and instead of sorting it for future reference in tags and categories (the complete opposite of “…a clear and sufficiently granular taxonomy…“) they should be gmailized – stuff them in an index and search.

Ah, you say, just embed a blog search box. Sure, but I have dozens of blogs I want to search in. So use some blogs search aggregator, you suggest. But I don’t want to get results from all the blogs out there, just from those I care about. Well, then, guess you’ll need to build yourself a custom search… or just use Delver. Knowing that in a few years every major search engine will integrate social features, I can carelessly blog about anything my social circle could find useful (say, how to plug an mp3 player to the audio system of an Israeli leasing-level Ford Focus), without bothering about categorizing with the perfect keywords (hint: there aren’t any). In fact, I think I’ll skip categories altogether in this blog, and just use tags for a nifty tag cloud

(crossposted on the Delver Blog)

Google Alerts sent me an email today with information about another reference to my blog name, Webnomena. This time it came from a service called Backtype. I had to follow the link! Every now and then I come across a new tool that fills my mind with numerous options and ideas. Twitter and Twitter search were two of those, MeeID is another, and now backtype is causing the same effect. Smart,flexible, simple, and useful.

In short, this is a service that crawl and collect comments from blogs and then organize them for you, using the comment’s URL. If the URL is your blog then all your comments from around the web are now in one place. More than that, you can see other bloggers and their comments too. The key feature is that you can follow other people’s comments (Twitter style).  Brian Solis from PR 2.0 wrote a great post about the true value of backtype in”BackType Unearths Blog Comments to Identify Relevant Conversations”.

There are more features that this service offers. The backtype blog also tell us that Mike Montano and Christopher Golda the founders are hard at work adding more cool stuff.

Yet, in this post I want to focus on how backtype suppots the blogsphere.

The tasks that backtype can help us with:

• Finding what the professional bloggers are reading and caring about – for instance here is where Om Malik hangs out.
• Improving comments’ quality – maybe we will see less instances of “great post, now please come and visit my blog”
• Finding implicit connections to complete one’s social graph picture. A comment is one way connection between the reader and the blogger. If the blogger responded or the blogger left comment on the reader’s blog then the connection is now bi-directional. They may not be linked as friends, fans or follower though. Social graph search engines like Delver and Nsyght can use this information for adding more connections (from like minded people) and enriching their search content pool.
• Complementing the Web-Conversing-Now dashboard – joining Twitter Search Trending Topics telling us what is hot now.
• Finding smart people – I notice some cases were a comment was better than the target post.
• PR – Using micro site like backtype for building great web presence to help your business – Danny Brown provide insights on this new trend in his recent Are Micro Sites the Next Wave of Business Promotion? blog post.
• Listening – having more ears on the web in addtion to Google Alerts, Twitter Search RSS feeds and more.

Brian Solis wrote:

“The process of listening isn’t only relegated to the research and analysis of individual reputations. Listening is also instrumental in the creation of new communications and service initiatives as well unearthing the specific conversations that matter to your brand – for gathering data and also discovering opportunities to respond“

I wonder if backtype have a plan to open their API for building new services around it!?

How do you plan to use this service for serving your objectives?

There are multiple ways today to find information on the web. There are different kind of information to search for. The  search task experienced could be overwhelming, frustrating, long and tiring or fun, efficient and successful. It is helpful to think through the search keywords, the search objectives, the type of information and the source of the information beforehand. I listed in this post my most frequently used searching tools. I also added a table mapping some of the possible search tasks to the tool that I think is the best for accomplishing it. 

My top 9 search engines:

1. Google – what did you expect?
2. Del.icio.us – the social bookmarking web site
3. Twitter search (formerly Summize)- dipping into Twitter’s archives
4. Twingly – spam free information stored in blogs
5. Technorati – blogs, tags, rank
6. Delver – social graph and search engine
7. Xoost – social search engines
8. Stumpedia- social search engines
9. LinkedIn – yes, the networking tool

Mapping search tasks to proper search tool

I hope that by writing this post I can help people to become aware of their search activity and the available options today on the web. I will be happy to hear about more search tasks, objectives and tools.

• Delver should add Mybloglog to their “Locate your Profile” section – it will help them building a wider social graph by drawing from a reach network. It may help them finally find and associate my blog with my profile. If not, at least let me add/claim it by myself.
• Muxtape is cool. Its simplicity is like Twitter, attractive. And the same as Twitter it needs something like Summize (now Twitter Search) for finding cool and matching mp3 mixtapes. I’m very curious to see where this service is going.
• Xoost and Stumpedia, two social search engines that are powered by human, needs some way showing who has good searching skills in specific area. It will be great to be able to ask for help in a search task from someone that already trawl the web, looking for information in certain domain of knowledge. It will be nice too, to be able to say thank you for a great find.
• Twhirl will save me the trip to Twitter’s web page looking for my followers count if it adds this value somewhere in the Followers tab. I cherish any new follower to  my Twitter’s Blogmon account and this is the only reason that I visit the Twitter web page today.
• Techcrunh need to hire someone to process comments in full time:). There are 685 comments and counting to this latest post: We Want A Dead Simple Web Tablet For $200. Help Us Build It. It is amazing to see that when you try to change something there is a strong reaction for both better and worse.

Read more at TechCrunch.com.

Are we going to see a new alternative added to the growing list of online content search solutions (I wrote about some of them here)?

So what do we have so far? popularity search engine (Google), semantic search engine, location aware search engine, blog search engine, spam free blog search engine, social search engine, conversational search engine, visual search engine, passive search engine, social bookmarking search engine. Did I miss any?

The new alternative is a profile based search engine. I’m not talking about Google Web History solution for personalized search engine using historical searches for better qualifying search result. It is a great idea though. I’m talking about using information, about me, from my profile, to better fit results to my search query. For instance if I’m a programmer searching using the Hibernate keyword I don’t want to see results about bears. If I’m a doctor querying about Viagra I don’t want to get all the spam in the world from now till eternity. If I’m 20 years old my world is different than the life of anyone else (if you call it a life after 30:)). Now these examples are for static profile attribute, yet there are many dynamic qualifiers that can be used to improve search results. One example was implemented already in location-aware search engines using your where about. Another is using your network dynamics like what that Delver is trying to do.  

It was the Web 2.0 applications with Facebook, as one of its leading example, that helped us to realize that one’s profile is not just name, email and contact. Your network (friends and groups), media choices (pictures, movies, books, and games), and activities (feed, expending your network, and participation) is also part of who you are. So, why don’t use it to help us find relevant content?