Out now from Microsoft Research is U Rank, an experiment that allows people to move results around, as well as share them with friends and add comments to listings.

Want to play? Sigh. You have to register using a Windows Live ID. Why not just make it open for anyone to use on a cookie basis? The cookie could allow short term changes to be remembered, while logging in could be done for those who wanted to protect their edits in the long term.

Anyway, once in, you can do a search and wait, and wait, and wait to get back results. Perhaps the response time will improve. When they appear, you can then hover to the right to get options like this:

These allow you to:

• Move an item up or down (click on “move me,” then you drag to where you want)
• Copy an item to another search (this is weird, and I couldn’t get it to work. I assume it will move a listing to appear in the results of another search that you indicate)
• Add a note to any item (this also wouldn’t work for me, perhaps because I’m using Firefox).
• Delete a result

Edits that you do then are recorded and appear the next time you do a search. IE, move a result higher, and you’ll see it higher next time you search, along with a little flag indicating you did an edit.

By default, sharing of searches is on, as you’re told next to the search box:

This means any search you do is automatically shared with those you friend through the service. That’s a terrible idea, having it on by default. I think people will fail to remember to block sharing for some fairly personal searches they do. Far better to make sharing something you do explicitly, to be on the safe side. Of course, the downside to this is that people might not remember to do so, which takes away from the social experience Microsoft is aiming for with this test.

Is search really social? My Search 4.0: Putting Humans Back In Search from earlier this year argued that in many cases, I feel it is not. It also touches on the privacy issues raised with sharing:

Finally, privacy is an overlooked issue when it comes to social search. People often search for intensely private, personal things using search engines. Search engines are almost like confessionals, where people seek solutions to problems they might not tell real people that are close to them. With social search, do they have to remember to turn off a sharing feature that might be activated by default? And if it’s not on by default, will it get any take-up at all?

Of course, you start the service with no friends at all, so sharing isn’t that much a worry at the start. At the moment, once you join, others that seem to match you are somehow suggested (I had suggestions from people within Microsoft Research). You have to actually accept them, however. You can also invite two other friends, at the moment.

Friends are able to move results that influence what you see. In other words, if they do an edit, you’ll see that edit in your own results. Plus, you can make use of a tag cloud of searches to see what others are searching on.

Concerns on sharing aside, the project looks interesting. It also looks a lot like the Google Like/Don’t Like experiment that has been off and on since last year, as well as editing tools that Wikia Search rolled out and ones that I think Mahalo also has. Hakia also just recently rolled out a way for group edits in the form of “trusted results” using librarians and informational professionals to contribute.

In short, the wave of letting users edit their own results is back? Back? Yes, because if I had time, I’d add more about how we used to have many of these same tools with the major search engines years ago. But no matter — the ability to edit and move results is welcome, and I hope we’ll see more of it.

And will we see this on Microsoft Live Search? Says Microsoft in their email about it:

Live Search has absolutely no intention of implementing this…

Of course, I’m sure Live Search almost certainly WILL implement anything that they find useful on this experimental site. After all, the email goes on:

The goal of the research project is to learn more about “how” people use search technologies, like whether they take advantage of the ability to edit search results and how they share the results over time with friends and family.

So check it out. Just remember to turn that sharing off or be sure you really, really like the folks you friend on the system, if you use it much.

Microsoft has experimentally introduced “SearchTogether,” which allows people to use a browser plug-in (IE7 only) to literally collaborate on search. You need a Windows Live ID and Messenger, but you can apparently use any search engine you like.

Here’s a tutorial and some screens.

I haven’t used it so I can’t report on the experience. Conceptually, however, I could imagine that in many use cases (e.g., Travel and Local Search) this could be highly valuable as an option. Microsoft Messenger has had collaboration capabilities around local search and Live Maps for some time, but they’re little used.

Indeed, collaboration is widely available in various online applications; however, it has yet to come to search. The key to mainstreaming this or gaining decent consumer adoption is bundling the option into the browser and not having several “hoops” for users to jump through.

This is very early and will undoubtedly evolve.

Previously I’ve covered what I dubbed Search 3.0, how search engines have evolved toward blending vertical or specialized results into “regular” web listings. Today, the step beyond that: Search 4.0, how personal, social and human-edited data can be used to refine search results.

The Search Evolution So Far

Before going ahead, let me summarize what I covered in my past article, in terms of how search engines have changed over time to create and rank the results you get when doing a search:

• Search 1.0 (1996): Pages ranked using “on-the-page” criteria
• Search 2.0 (1998): Pages ranked using “off-the-page” criteria
• Search 3.0 (2007): Vertical search results blended into regular search results

The evolution above is not perfect. For one thing, some “Search 3.0″ blending started to happen years before 2007. It’s just that in 2007, I felt all the major search engines made the leap into Search 3.0 in a significant way.

As for Search 2.0, looking at off-the-page criteria such as links, Google kickstarted that heavily in 1998. However, some link analysis happened before then, and all the major search engines probably didn’t get on board to using it more fully until 1999-2001. But the launch of Google in 1998 remains the benchmark year in my mind, for that particular change.

The evolution is also only applicable to crawler-based search engines, those that use automation to gather web pages, store copies of them and search through the compiled index to create listings for searches. Yahoo was a major player using human power before 1996 and continued this way for years. Indeed in 1999, a majority of major search engines were presenting human-powered results. This quickly changed as Google grew. Yahoo made its human results “secondary” to crawler-based ones (then provided by Google) in October 2002. Today, all the major US-based search engines depend on crawler-based results.

To cap off the caveats, the evolution above is not the only way search engines can evolve. That’s just how things have largely gone with US-based search engines, which in turn tend to also be the major search engines for most countries around the world. There are exceptions. For example, Naver is the dominant search engine in Korea — and there, listings are largely human generated.

Search 4.0: The Human Factor

Onward to Search 4.0! As I said in my opening, to me this is the move for search engines to make use of human data as part of their ranking systems. In particular, it means human data generated by you, by those you know or by human editors.

Search engines already make use of some human data. All the major search engines, for example, monitor what we click on within the search results. This helps them determine if a particular listing is drawing more or less clicks than would be expected for the position it holds. For example, if the number two listing for a particular query is getting less clicks than “normal” for a listing in that spot, perhaps it’s a bad quality listing that should be replaced with another.

Another example: all the major search engines make heavy use of link data — and that link data is largely human data, humans both “voting” with their links and “tagging” pages by the words they use in the links. Google Now Reporting Anchor Text Phrases and Google Kills Bush’s Miserable Failure Search & Other Google Bombs provide more about how links are used in this fashion.

When I talk about putting human data into search results as part of Search 4.0, I mean things that are more aggressive or active than what I’ve covered above. I’ll start off with the most refined Search 4.0 implementation out there, Google’s personalized results.

Google: Search 4.0 Gets Personal

With Google Personalized Search, the web pages you visit, bookmark and things you click on within search results at Google are used to custom-tailor search results for you. The personalization is not as dramatic as with a place like Amazon, where if you purchase a book once, Amazon seems to continually push similar books like that at you forever. Shifts are far more subtle, mainly to help elevate results from sites you frequently visit.

To understand more, these articles go into depth about the process:

• Google Ramps Up Personalized Search
• Google Search History Expands, Becomes Web History

I’m fairly bullish on personalized search as an important addition to other factors (Search 1.0-3.0) in improving results. For one thing — better or worse — people often judge the relevancy of search results based on ego searches. Does a search engine find your home page, blog and related material when you search for yourself? Does it find your company? Personalized search is an ego search reinforcer. Because you go to your own places on the web often, Google senses that you want them to show up higher in search results, and they do. It’s a genius way to ensure anyone reviewing the service comes away pleased!

Of course, fulfilling ego searches can also be an relevancy advancement, not just a marketing ploy. There’s an excellent chance you’d have better searches if sites you visit more often get a bump in the search results. Personalized search can do this. In addition, over time, personalized search can potentially figure out other sites that are similar to those you visit and give them a relevancy boost.

Since Google expanded personalized search last year, there’s been one further major development. Personalized search uses searches over time to refine results. However, Google also has a system it is testing to refine results based on the last query you did, even if you aren’t taking part in the personalized search program.

“Previous Query” Refinement Coming To Hit Google Results explains more about how this works. It’s been used to improve the ads shown on Google for almost a year now, and it’s currently being tested to refine regular results. Google said that previous query refinement has been one of the strongest signals on how to personalized results so far.

Social Search: Promise Or Hype?

Last year, blogger Robert Scoble kicked off a round of “Facebook’s gonna kill Google” with a series of videos suggesting that because Facebook knows who your friends are, they’ll be able to apply that “social graph” data to improving search results.

The Promise & Reality Of Mixing The Social Graph With Search Engines was my response, a bucket of cold water explaining that using social data wasn’t some new idea that had never been tried before. The article went into depth explaining how Eurekster and Yahoo both assumed search could be “socialized” similar to photo sharing or bookmarking, only to find that wasn’t the case.

Yahoo had little take-up of its social search product. I’ve never seen the company explain why. My own suspicion is that take-up was low because search is NOT a social activity. I believe people tend to search when they have an immediate desire that needs fulfilling, and taking time away from the search activity to “share” with others is a distraction. Consider the person who has a broken water pipe. They might search quickly to find a plumber. They aren’t likely thinking at that moment that they want to tag and classify the search they conducted, much less the plumber they called. They just want the pipe fixed!

Eurekster has said that it found social activity worked better when people organized to build what it calls “Swickis,” search engines that hit only a custom collection of web sites related to a particular topic. Earlier this year, Eurekster formally came out of beta. However, the service has been entirely off-line for almost a week now. Practically no one has noticed, which speaks volumes to its usage and that aspect of the social search potential. Twitter, which some still view as a niche service, can hiccup for an hour and produce reams of blog attention. Eurekster goes silent, and the web stays silent about it.

I’m A Facebookholic & I Have 5,000 Friends

Still, couldn’t Facebook have more luck? For the record, when I spoke with Facebook director of engineering Aditya Agarwal about social search ideas last December, he was far more realistic than outsiders who hype what Facebook could do. In particular, he wasn’t certain how useful the social data actually would be for refining web search.

I plan to do a future article with Agarwal to explore this more. As a reminder, Facebook right now has no web search feature at all. And while it does have an ad deal with Microsoft, our previous Microsoft’s Facebook Ad Deal Doesn’t Include Search article covers how a search partner hasn’t been selected.

Let’s assume that Facebook does select a search partner, which it will need, since trying to index billions of pages and serve millions of queries each day is not an easy task (just ask Microsoft what it’s like to build that from scratch). What could it do with social data?

For one thing, it could monitor what people are clicking on in a potentially more “trusted” environment. Anyone can use web search anonymously, even sending in clickbots to make it seem like some particular listing is super hot. Having to register to be in Facebook and search from within there might make the clickstream data less noisy. But then again, it’s still a fairly open door that someone can walk through, if they want.

Facebook could tailor results based on what friends are searching on. If it knows what you and your 25 friends all seem to select from results, it could ensure those sites get ranking boosts for future searches. That’s very similar to personalized search, except it sounds full of extra friend-goodness, right?

The flaw here is plenty of people have friends on Facebook they don’t know. Some people collect friends for fun (and profit). Some people get friended by others just looking to build up their profiles. Some people you might friend not because you like them but because it’s easier to friend them than say no. Any of these instances can cause “pollution” of the social data that supposedly was going to improve your search results.

Consider also the case of someone who might work at some very conservative company but outside of work is a freeliving, devil-take-all person. Do they want coworkers who are friends to flavor their search results or those friends they hang out with when work is over?

Finally, privacy is an overlooked issue when it comes to social search. People often search for intensely private, personal things using search engines. Search engines are almost like confessionals, where people seek solutions to problems they might not tell real people that are close to them. With social search, do they have to remember to turn off a sharing feature that might be activated by default? And if it’s not on by default, will it get any take-up at all?

In the end, I think there is some potential to tapping into a social network and applying it to search. However, I still remains uncertain how that will unfold. It especially remains uncertain that this is somehow the secret sauce for anyone to jump past the current state of search.

Return To Humans: Hello Mahalo!

Earlier, I’d mentioned how Yahoo started off using human beings to create its search listings in the days before Google existed. Over time, the human soul in search was lost to reliance on the supposed scalability of machines. Anyone who wants to see how much we’ve handed over to machines need only search for buy cialis online on Google. At the moment, the results are littered with online discussion forums that have been “borrowed” by affiliates and others hawking deals.

Those pages will sit there for a day or two or three or potentially weeks, as Google usually tries to find an algorithmic solution to getting rid of them. The idea is you might have to suffer a bit in the short term until a long-term cure is found. But then like a virus that mutates, something else gets through, requiring a new long-term cure.

Enter humans. A human editor, reviewing results like that, can immediately spot junk that should get yanked. Even better, a human editor could act as a curator. How hard can it be to find 10 quality sites that should come up for that or other terms?

That exact human solution, of course, is what Mahalo has been banking on. Mahalo, launched last year, uses human editors to hand-pick top results. For background on the service, check out these past articles:

• Mahalo Launches With Human-Crafted Search Results
• Mahalo Greenhouse: Get Paid For Writing Search Results
• Mahalo Follow: Toolbar Gives You Human-Powered Alternatives To Searching, Surfing
• Mahalo Adds The Social Graph To Search
• Mahalo Adds More Social Features

As part of a talk I do on Search 3.0 and Search 4.0, I have some screenshots from last year that illustrate well how a human can indeed do better than the machines, for some queries. Remember the fires in Southern California at the end of last year. After a series of wide ranging ones, Malibu was hit with a second one a month later. Here’s what those searching on Google got in response:

The news box at the top is great, but sometimes searchers skip past things like this and go to the first “real” result. That’s a story about the Malibu fire early in 2007, not at the end of the year. Other results were largely about the fire of October 2007, rather than November 2007 (which is what many searchers at the time I snapped this would have been interested in).

Here’s Yahoo:

Again, news results at the top, then unlike Google, places you’d expect to find news about the fire — the local paper; ironically a map of the fires on Google Maps that Google itself didn’t return; the Malibu city web site, as well as the fire department.

Here’s Microsoft Live Search:

Very similar to Yahoo — a news box, the fire department, the Red Cross. What’s not to like? Well, let’s look at Mahalo:

Note at the top that Mahalo’s human editors understand there’s a different fire that happened in the past, in October 2007, and offer a link to a page about that. Then there’s a nice list of news sources, followed by coverage by date. Over to the side, a synopsis of the current situation. If you could see more of the page, there was lots of other categorized information.

It’s nicely done. It’s very helpful. And it was created with a human thinking about what other humans might want to see, rather than machines guessing.

Scaling Humans

So is Mahalo founder Jason Calacanis onto the Google-killer, human crafted results? No. I think human review can be part of the solution, part of the Search 4.0 addition to what we have out there already — but humans can’t craft pages for every possible search. In addition, it’s hard to keep those pages maintained once they’ve been made. It’s also easy to cross over from being a search resource that points to other resources to becoming instead a destination site. I think a good search engine avoids that (and Who’s Ranking For Knol? Hello, Wikipedia! has more on this topic).

Mahalo can also be overwhelming. Try a search for hillary clinton and there’s category after category. Background links. News links. Photos. Videos. Bio links. Blogs and message boards. Plus, there’s even more. I think at some point, you want your search engine to make some key choices for you, not flood you with so many that you don’t know where to begin.

Another issue is that what Mahalo’s human editors do, machines can get close to. Hakia especially stands out here. Search for hillary clinton there, and you’ll see how listings are grouped into categories like Awards and Biography without humans being involved (and see Social Networking Through Search: Hakia Helps You Meet Others for background on how Hakia works).

More Humans

There is another major search project involving humans: Search Wikia. Backed by Wikipedia cofounder Jimmy Wales, the service aims to involve humans in rating pages, annotating them and helping determine the ranking algorithm for choices the machine side of the project makes.

The articles below have more background on the service:

• Search Wikia: Not Even A Remote Threat To Google
• Search Wikia Adds Alpha 0.2 Features & More

Right now, the quality of the service is poor, as Search Wikia itself readily admits. There’s still lots of work to be done — and even with it, it might never succeed. But allowing humans into the process is, in my view, a good thing.

Indeed, even Google understands this. Last year, Google started doing some education about how human “signals” are already incorporated into its algorithm (see Google’s Human Touch and Google & Human Quality Reviews: Old News Returns). Aside from this, last year it also started testing a way for people to annotate search results — add those they like, remove some, suggest other ones.

Watch Personalized Search

Overall, there’s a role for humans, a way for them to be in the search process to enhance results. Actually, there will be several ways for them to be involved. Exactly how remains to be seen, of course.

Of the things I’ve outlined — personalized search, social search, human editors — I think personalized search is the one that will emerge as the major part of Search 4.0. That’s not to discount other things being tried, and they’ll contribute in some ways. But to me, personalized search has the most potential for another big relevancy leap. We’ll see!

At our SMX Social Media Marketing conference this week, we had a great panel on the future of human powered search. Jason Calacanis of Mahalo, Jimmy Wales of Wikia Search, and Steven Marder of Eurekster all took part. Jason had some remarks on SEO that set off the usual wave of upset. But as I commented to those who weren’t at the panel, by the end of Q&A, Jason – along with Jimmy Wales and Seven Marder — were agreeing about the usefulness of SEO. It’s all down to the definitions.

Below you can hear Jason’s presentation yourself, then you can hear the Q&A portion that covered search marketing and human powered search. Note that the video production could be better. Hey, I just got a Macbook — it’s my first time playing with what it can supposedly do. Do don’t hassle me over the titles that could be better. Also, I will get the entire session up with the presentations from Jimmy and Steven, along with the further Q&A. But first, Jason:

The Q&A:

And here’s some of the reaction to initial reports of his comments, before anyone not at the show could hear what he said in full:

• My Plea to SE Conferences: Turn Calacanis Away, Eric Lander
• My Plea to SE Conferences: Turn Calacanis Away, Sphinn discussion on post above
• Why Calacanis Should Not Be Allowed to Speak at Conferences, Slightly Shady SEO
• Why Calacanis Should Not Be Allowed to Speak at Conferences, Sphinn discussion on post above
• Jason Calacanis has pissed off SEO’s… again. Seriously., Sphinn
• Srsly are SEOs Really That Easy To Manipulate?, ShoeMoney

And here’s further coverage of the show (for initial coverage from the first day, see SMX Social: Mahalo To Do Microformats, Search Wikia Adds Alpha 0.2 Features & More):

• 20 Take-Aways from SMX Social Media – Scott Clark, Scott Clark
• Evangelist – The Marketer’s Role in SMM, BruceClay.com
• Micro Communities, BruceClay.com
• Wikipedia, Yahoo Answers & Answer Sharing, BruceClay.com
• Social Media Expo (SMX) Report from Long Beach, California, Wpromoter
• SMX Social Media Conference in Long Beach, MindCradle
• Rand Fishkin | Jason Calacanis | SMX Social, Gary Pool
• SMX Social Media Coverage Round Up, BruceClay.com
• SMX Social Media Pics, Scott Clark @ Flickr

News from the first day of our SMX Social Media Marketing conference. Mahalo is now doing microformats as a way to enhance its search results and allow local businesses to be added to your address book. Wikia Search has gone to "Alpha 0.2" with new features. And other conference coverage, below.

Jason Calacanis of Mahalo, Jimmy Wales of Wikia Search, and Steven Marder of Eurekster all spoke at the end of the day on our Social Search: The Human Challengers panel. Lisa Barone provides live blogging coverage in her Keynote – Social Search: The Human Challengers post at the Bruce Clay blog.

During that session, Jason shared that Mahalo is providing microformat support. Mahalo Adds Microformats from Mahalo’s Sean Percival covers how this works, complete with screenshots. Check it out! See also further discussion on Techmeme.

Also during the session, Jimmy Wales shared that Wikia Search has added several new features as part of a new alpha release, one he dubbed "Alpha 0.2." Wikia Search doesn’t seem to have news up yet on its own site, but Wikia Search Launches Major Enhancements to Search Alpha from CenterNetworks covers how you can do things like preview pages and copy sections of them into search results. You can also add related search terms to queries, add pages you think should be added to a search result, and delete material you think doesn’t belong.

Scary, freaky? You bet — but Jimmy also covered how, like Wikipedia, the history of all this can be seen, which potentially provides some self-policing. Try the new features on the test site here. And further discussion can be found on Techmeme.

For other session coverage from the conference, see:

• Social Media Marketing Essentials, BruceClay.com
• Linkbait – Chumming for Traffic on Social Media Sites, BruceClay.com
• Extra! Extra! The Social News Sites, BruceClay.com
• A Marketer’s Guide to Social Bookmarking, BruceClay.com

We’ll also do a Day 2 post tomorrow with further coverage and add to it anything we’ve missed. Also, you can keep up with live blogging both at the Bruce Clay blog and via Twitter. Yep, Twitter. Michael Gray is diligently microblogging on our SMX twitter account.

I thought I’d gracefully retired from the Danny Sullivan Keynote Review business. Comparing Danny to Edward R. Murrow, assessing how the attendance stretched the room capacity… ahh, they were good times. Then I awoke to find myself in a large hall at the Santa Clara Conference Center. It wasn’t a dream! Turns out I was only a bit sleepy because that O’Hare blizzard delay caused me to land in San Jose at 3:00 a.m. on this day, February 26th, 2008. It was now 9:01 a.m., and a fresh Sullivan keynote was beginning at SMX West. There I was, sitting over to the right-hand side of the room (Danny’s left) near where Matt Cutts was hiding. Laptop open, battery charged. Danny speaks. As if this were liveblogging, which it isn’t, I now switch to the present tense.

The topic: Search 3.0, Search 4.0, and Beyond. To cover this — surely not, 4.0?? we’re still hearing complaints about Web 2.0! — Danny will have to cover 1.0 and 2.0. This is straightforward stuff for experienced search marketers, but brand new to many. Danny is talking about how Search 1.0 was primarily about on-page factors like keyword density and keyword matching. This was so unsophisticated, it more or less launched the practice of gaming the search engines. In those days, pre-1998, search engine optimization as Danny taught it through his Webmaster’s Guide to the Search Engines was relatively rare. In our hazy memories, we seem to remember, on one hand, ordinary marketers and webmasters making good use of Danny’s detailed, common-sense advice about how various search engines ranked content; and on the other, those who took that advice and ran with it in all sorts of evil ways. All will agree that Search 1.0 might have been a golden age for index spammers, but from the standpoint of the searcher, it sucked.

Moving onto Search 2.0, Danny is addressing the move by the search engines to address the link structure of the web, treating a link like a vote. While the most notable pioneer of this trend was Google with its PageRank method, Danny is also mentioning Direct Hit, which looked at click paths to assess the apparent popularity of different search results.

Already I am beginning to sense the difficulty of slotting particular search innovations into generations of x.0’s, given that Direct Hit’s methodology started to encroach into the types of behavioral analysis and quasi-personalization that would later be more fully developed under Search 3.0 (or 4.0, or 5.0, depending on who you ask). I won’t belabor the point but it seems to me too many .0’s here, and there’s a problem of implying a sort of temporal or generational shift where in some cases they may be just different aspects or types of search or different metaphors or philosophies that may not need to be relegated to the scrapheap just because they coexist with other (or newer) stuff.

Anyway, back to Search 2.0. Danny is pointing out that it didn’t take long for the optimizers to figure out how to game this supposedly bulletproof generation of search. You had the playful approach — Googlebombing — which would merely produce an embarrassing result on a non-popular query. But more fatally, you had the development of link farms and the link economy, developments which Google denied for years.

Danny is now turning to discussion of Search 3.0 – Universal and Blended Search. This is the phenomenon whereby search engines will increasingly mix and match other types of search results along with their standard Ten Blue Links search engine results page. For example, Google might place a YouTube video search result in position 5 on the page. Because the still image requires more space than a standard result, this takes up quite a bit of screen real estate, and pushes the remaining SERP’s that much farther down the page below the fold. A Google News result in position 10 takes up one more space that formerly would have been in the search marketer’s organic index playing field.

Spelling out the inadequacies of 1.0 and 2.0 actually serves as a pretty interesting piece of insight into why there is so much focus on 3.0 now. The official reason might be that users respond well to being provided with a variety of different types of information, a variety that can be refined over time by paying attention to click patterns. An unofficial reason, though, might be that Ten Blue Links often don’t withstand careful scrutiny on their own. The organic index is spam-ridden, user queries are often hard to disambiguate, and search algorithms are often failing to show definitive results. Finally, gen-2.0 search algorithms may over-reward “SERP staples” like Wikipedia, and fail to alert users to in-house content that the search engines might have spent much time developing.

Now Danny is illustrating the concept of Google Universal with a screenshot that shows a large number of local search results, totally dominating the area above the fold and pushing everything down the page. He’s saying “and look, they’re the top results, so you’re very likely to click on them.” I am now thinking to myself – “geez, they always do this! Grehan does it too!” This example clearly shows that local results are the second-highest results on the page, not first highest! Three attractively-positioned premium sponsored listings are taking up quite a bit of real estate above that, and are further highlighted with a goldenrod background. Moreover, about 4.5 sponsored listings are visible above the fold in the right margin, and they look pretty attractive too. I always have to remind these organic search gurus of this stuff!

Danny is also tipping his cap to blended search efforts at other search engines, such as Ask 3D and Morph. He gives a useful Yahoo example, showing how they blend in an event result from Upcoming, to give the search results page a fresh feel.

We’re barely into Search 3.0 and we’re now looking at making the so-called “social graph” and other elements of personalization a big part of search. This is Search 4.0, if you will. Danny is having mixed feelings about some of it.

Personalization can emanate from data the engines collect from portal pages such as iGoogle (funny, Danny doesn’t mention Yahoo’s personalized home pages dating back much farther); users bookmarking things specifically using the search engines’ bookmarking features or delicious (you’d also want to wonder about browser integration, web-based bookmarking 1.0, and also about the so-called P2P Search era, and why those failed to take hold whereas now the SE’s are seen as clever to integrate them?); search histories showing you what you’ve clicked on in the past; and web history or clickstreams. Danny says that Eurekster and Yahoo 360 haven’t succeeded yet. So, what are we to make of Search 4.0?

Danny is hitting a slightly skeptical note when it comes to talk of Facebook and the social graph. I think it is useful to play devil’s advocate here, and it doesn’t conflict with a recognition that the information could help advertisers and users. Danny’s light critique of this realm is the question of how seriously you can take a community where people have 5,000 “friends.” But those gathering data don’t need to take it all seriously — they could measure activity. The biggest problem with what Danny is calling “monitoring clicks in a trusted environment,” of course, is privacy. Bang on. If the social graph is tantamount to spyware, then for every significant action or advance, there is likely to be a reaction or retreat.

For the benefit of new attendees, Danny is fleshing out his talk to cover other key ideas that everyone should know about, but I’m using the time wisely to write the wrap-up paragraphs to this column.

I very much hope Danny reprises this exact same talk next year, but hopefully sooner. Many of those who come to the table with a little search knowledge that is now six years out of date can be dangerous and hazardous to work with. Except for a couple of nanoseconds as he discussed Search 1.0, Danny proffered nary a mention of meta tags, page titles, heading tags, or keyword density. Some of those basics are important, but they don’t constitute anything other than a starting point towards good rankings. Nor did he bore us with nonsense about reciprocal link pages or other warmed-over gimmicks.

Rest assured, the scam cold callers will keep the very least relevant search optimization techniques on the agenda for those impressionable enough to pick up the phone and listen. It’s a marketer’s job to stay current, though.

Search engines are getting smarter every year. Search marketers need to keep pace, and Sullivan’s keynote is a great way to introduce the beginner-to-intermediate audience to the cutting-edge stuff, from an authoritative source. I won’t be surprised if, next time around, I see a few attendees being literally dragged in by the ears. Listen to what Danny’s saying! You can’t go wrong.

Andrew Goodman is the founder and principal of Page Zero Media and author of Winning Results with Google AdWords. The Paid Search column appears Mondays at Search Engine Land.

VentureBeat has a nice Q&A with Google’s Marissa Mayer on how the search engine is considering using social data to improve its search results — what I’ve described as "Search 4.0" as a generational jump in my Search 3.0 article from earlier this year. Some highlights below:

• Social search is hard, in that many searches are sensitive, so letting people in your network know what you are looking for raises serious privacy issues.
   

• Google might try doing more with labeling or tagging results as a way to implement social search. Marissa talked about the existing Google Co-Op service being a success this way in terms of health searches. Personally, Co-Op seems to have largely failed to catch on, from what I’ve seen. We also know that Yahoo’s experiment with tagging search results didn’t go very far (see The Promise & Reality Of Mixing The Social Graph With Search Engines for more on that).
   

• Marissa raised the idea of Amazon-like recommendations. To some degree, Google already does this through its personalized search feature. However, the suggestion is that Google could allow you to have a network of friends and contacts, and that their searching activity could be used to influence what you see. Again, Yahoo (among others) had thoughts of doing exactly the same thing years ago but never moved forward with it.
   

• Gmail comes up often. It’s clear Google continues to see Gmail contacts as a starting point for implementing any type of social influenced product. Google The Stealth Social Network? has more background on some existing moves the company has already done in this space.
   

• Would Google let others bring their social data to the table to re-rank Google’s results? That doesn’t seem likely based on the interview, with Marissa in particular talking about how other sites using Google’s core data aren’t allowed to re-rank it.

Be sure to also check out Eric Enge’s interview from earlier this month with Grant Ryan on social search. Grant’s from Eurekster, the real pioneer in social search. How the company thought social search might go changed when it collided with reality (a lesson many fail to remember when they get excited about social search). A key part:

As far as social networking and integration of search, as you are probably aware, we (Eurekster) had a deal with Friendster around social networking search. That was one of our first deals. The first crack at the idea of social search was that a lot of people find information through people they know. We actually did an implementation with Friendster, where everyone had different search results based on their friends and friends of friends, and that was quite a sophisticated application.

But, people have got so many friends that look for different things that it’s kind of all over the place. What happens within groups of users? This gave us the idea and impetus for the Swicki product, which is now the core focus of Eurekster, where if you have got a topic and a group of people that are interested in that topic, that’s where the group of the people watch for what they could be learning from them. That’s where it really adds a lot of value – at the social level.

I’m still working on my promised "Search 4.0: Social Search" piece to recap some of the latest moves and where things may be going. I’d better get it done! But my aforementioned article, The Promise & Reality Of Mixing The Social Graph With Search Engines, has lots of background here, especially from the middle part down.