Everyone wants to be social these days. Given the hype around social networks and social bookmarking, it's little wonder that search wants to be social too. The problem is that when you refer to "social search," the odds are that whoever you're talking to will have a very different idea of social search than you do.

Social search should be defined as the process by which a site's community of users influences the algorithmic search results displayed for any one of those users. A classic example from several years ago was when Eurekster powered Friendster as of 2004. Back then, you could see the most popular searches from your friends, and search results viewed by your friends appeared at the top of your results page. It was a model for social search, until it was discontinued.

Over the years, Eurekster has kept up its focus on social search while taking it in new directions. It has been focusing on its Swicki product, which is essentially a custom search engine using social search. A Swicki's creator, generally a blogger, defines the sites to include or exclude and other parameters, and then searchers can vote on the results. Eurekster also shares the most popular searches for that community's engine. On Read/Write Web, the hot searches are "mobile," "next generation," and (woefully ) "web 2.0," while on TechCrunch the hot searches are for "$5 million funding," "aideRSS," and "grockit." All the while, the Swicki learns from searchers' behavior.

I asked Eurekster CEO Steven E. Marder for some color on Eurekster's evolution. He said, "When we started, we thought that leveraging social networks that were built on FOF (friend of a friend) would provide an active and robust set of user data that we could use to refine and rerank search results. What we found is that your pure social nets are so broad (as your friends have many interests -- and not necessarily expertise in those interests), that the search results would be influenced and skew, and would be of interest to searchers BUT not necessarily be more relevant. However, we have found that applying our technology in a social network environment makes great sense if within vertically oriented or geo-targeted oriented groups within those networks. Friendster was too unstable as a company and a platform for us to effectively roll this out with them."

That means that with social search, it's not who you know, but who you share affinities with. If I'm searching for travel recommendations, I'll have more in common with other readers of a blog about exotic travel destinations than I will with most of my friends. The wisdom of crowds has its limitations. If I were to pick a honeymoon spot based on where all my friends are going, I'd wind up in Hawaii. If I were to pick a honeymoon spot based on the results selected from other people choosing more unusual honeymoons, I'm more likely to find recommendations for India, Chile, or some other place that my social network as a whole would not come up with.

That's what social search is supposed to be about. Yet listings for social search sites, such as the comprehensive list on Mashable, are littered with sites that have no business being there, Given some of the confusion over what social search is, here are some examples of what social search isn't:

· Human-powered search: Human-powered search engines are barely search engines, let alone social search sites. With sites such as Mahalo, people write the searchable content, but they have no control over how it ranks in search results. Additionally, the search results rank the same way for searcher.

· People search engines: Searching for people has nothing to do with social search. Spock is a search engine for people where anyone can contribute to profile pages, but that's not enough to call it a social search engine.

· Live assisted search: At ChaCha, you can have a guide help you search, and there is some social interaction, but it's not the process of social search. And for the most part, it's a waste of time. Assisted search is slow, guides rarely know more if anything than the searcher, and the major engines are getting better at offering shortcuts to guide specialized searches.

That being said, there are still other examples of companies tackling social search in its truest sense. One great example is from Collarity, which runs on publishers' sites and learns from users' search behavior to recommend search terms specific to that site. In its partnership with Fox Interactive Media, you can see a perfect demo of how searches differ from communities, as Collarity powers Fox's local MyFox sites throughout the country.

When you type "rangers" in the search box at MyFoxDFW.com (for Dallas-Fort Worth), all the search terms it recommends relate to baseball, given that the Texas Rangers play in the DFW Metroplex. Yet when you enter "rangers" as a search at MyFoxNY.com, the first suggestions are "shanahan" and "lundqvist," players on the New York Rangers hockey team. Even more generic search terms show differences; while a query on "crime" brings up terms related to hate crimes for both cities, DFW's results also refer to "crime spree" and "crime scene" while NY's refer to "crime stoppers" and "violent crime."

What's important for Collarity is what other visitors of your local MyFox site are searching for, not how closely related those searches are to you. You might be a friendless hermit with no social network to speak of -- but with social search, you're never alone.