An Algorithm is a set of instructions or procedures for solving a problem.
In the same way that computer scientists 50 years ago focused on the single problem of designing a general purpose computer, there is a similar focus in 2005 among leading Internet service architects: creating a social media computer that leverages user generated content to automate the production of commercial content. In so far as this represents the important problem that the best and brightest of us are looking to solve, then to an extent it is a race for the best algorithm.
From PageRank to PeopleRank
Hovering over this endeavor is the shadow of the last great algorithm, namely Google search engine. At its core, Google is PageRank (which nominally cites both one of its founders Larry Page and its subject of operation, Web pages):
"PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page's value. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves "important" weigh more heavily and help to make other pages "important.”"
In this case, (1) the input for the algorithm is the population of web pages, (2) the instructions rank them in value based on their link structure, and (3) the output is the list of links that you see when you search for something.
Now transpose people for web pages, and you see how the race for the next great search algorithm has less to do with organizing static HTML content than with coordinating the constantly changing expressions of millions of distributed people. For an interesting perspective, see my fellow entrepreneur Mark Pincus's riff on the PeopleWeb. Many Internet businesses have tried to direct user behavior into certain architectures of participation. Services such as Friendster, Orkut, and even Pincus's own Tribe, presume to address all of a person's social communication needs in one place. All of these services, however, are now rapidly trying to reinvent themselves to stay relevant to a community that refuses to be intermediated by somebody else's system.
The services that seem to do the best job at enabling users to communicate on their own terms are those that manage to find a middle ground between the DIY (do-it-yourself) ethos that is beginning to pervade the web and the need for structure to guide constructive interactions (ie the reason by Wikipedia succeeds and most other Wikis fail). LinkedIn, with its two million profiles of professional affiliations, provides the tools for interesting social media production, even if the site itself limits one's imagination (open up the API please). The reason behind the annoying digerati blogfest on folksonomies (myself included) stems from the simple but mildly heretical notion that users, given decent primary (meta)data, might actually be able to create their own systems that scale. Clay Shirky (lighting designer for the Wooster Group, CTO of SiteSpecific, advisor to Flickr, current leading pundit for the digerati at shirky.com) captures the anxiety perfectly in his title to last week's panel at ETech: "Folksonomy, or How I Learned to Stop Worrying and Love the Mess". The question is, then, whether a PeopleRank algorithm that uses community driven tags as its input, could do to About.com, Gawker Media, and Weblogs what Google did to Alta Vista, namely deliver a superior end-user experience that requires only incremental server bandwidth to scale.