Each person we communicate with is unique, yet we share and are loosely connected by topics of interest. Instinctively we are drawn to communities that share our deepest desires. Leaders and trend setters pave the way for community formation by defining it's core and identity.
Defining ourselves by what we love
Imagine a human abstraction which ignores social standing, location, native language, wealth, and fame. Instead this representation focuses only on interests and depth, individual style and persona, and as much background knowledge and expertise of a person as possible.
Organic Community Growth and Separation
This way of seeing people as intelligent decision engines with complex interwoven desires masks out all non-essential information to the problem at hand, seeking relevance. Fluid communication is the result of series of questions and answers orbiting a central topic. Perhaps a better analogy is like asteroids orbiting the rings of Saturn, which ripple with waves as moons soar past.
Layered discussions arise as elementary interests are brought together into cross topic molecules. The result is communication between the intersection of those groups (and) or between members of the separate communities (or). In this way compound topics are aggregated into new elements and elements are broken up into separate fragments. Topic granularity is a function of time varying interest, knowledge, and definitions of the crowd. Conflicts are easily settled by individual capacity to "fork" any element*.
Today's Social Search
Our current methods of social search champion successful curators or popular people. It's etched into our biology and being to seek masters in fields related to our survival and satisfaction. We are drawn to speakers who we perceive capable of enriching our lives with their generosity, knowledge, and resources^. Topical authority is earned by the individual, yet granted by the community.
Network Health Requires Intelligent Routing
As the social network of humanity matures, the amount of cumulative knowledge generated and curated within it increases. Even now, at this nascent stage of the social web, individuals are incapable of perceiving the information and opportunities that swell around them with respect to a hyperfine niche.
Eager problem solvers in universities, startups, and big corporations wrestle with algorithms to bring order to exponentially growing data**. I'd like to step back and study how information finds us, and how we personally judge relevance.
Why does specific information reach us? What shape and form of message are we most open to at a given time? Is there an upper bound to relevance? If relevance is uniquely defined by each individual, each algorithm must be tuned to each person^^.
Here are a few fundamental assumptions I believe are in common to the finest relevance solutions:
- Ultimately the user must be in control of the filter</I>
- Understanding the dynamic social network and the individual is an iterative effort
- Time and location are tied to relevance. Solutions which take too long are penalized
- We should never be satisfied or settle for good enough when it comes to the quality of relevance </ul>
Notes:
*= thanks to git/github, open source, and historic processes for the forking analogy
^= In many instances of late, I've read the most potent ability of investors is their ability to introduce a new business to those mutually beneficial partners, be they follow on investors, new marketing channels, or customers. The network and introductions are proving more valuable and efficient than cash.
**= Brilliant folks are working to bring order to perceived chaos with semantic efforts, social search based on network interaction, feed subscription entities, and physical location. My6sense and my friend Louis Gray who's their brand new VP, Hunch, and Google magic immediately come to mind. Facebook and Google ads, along with Amazon suggestions are also driven by relevance as a bottom line to performance.
^^= Supersets of algorithms may give more relevant suggestions at the cost of generality. Explore the million dollar Netflix suggestion algorithm prize for examples of this.