One Degree of Separation

Social networks rely on your primary network - your existing friends and contacts - to introduce you to THEIR friends and contacts. Each of the people in the network are called 'nodes', each with one or more connections to other nodes. Each of those connections is sometimes called a degree of separation; a friend of a friend (FOAF) would then be two degrees of separation. The famous phrase "six degrees of separation" was based on work by psychologist Stanley Milgram who determined that any two Americans, connected in the nation-wide extended network, are separated by an average of five intermediaries, i.e. six steps or degrees.

Despite their connectedness, two people separated by so long a chain are extremely unlikely to ever meet. In fact, we usually only ever meet the friends of our friends: an extremely small fraction of the larger network. Web services like LinkedIn, the business contact network, tracks your chain to three degrees of separation - though I wonder how often the third degrees ever connect. [Friendster tracked the chain even further, and this pursuit has been credited with Friendster's downfall, as tracking long chains is very difficult computationally and has much larger hardware requirements.]

Online Social Networks are not really social, and the network - as degrees of separation - serves mostly to separate. So, if one really wants to 'kill' social nets, one needs to get rid of the 'net' (the multiple degrees of separation that separate people) in order to bring people together. Jyri Engeström argues that social networks should not be based on individual connections between people that can be counted and accumulated, rather people must be connected by shared objects. We agree and take this to the next level by making everything in the virtual community an object, where each object is connected to every other object.

The New Paradigm

As proposed in the last post, what is lacking in the current data islands and the proposed schema solutions is a way of harnessing the true power of the collective to actually reduce information overload and increase discovery. This will require a revolution in content and relationship discovery that can only arise with a completely new kind of information filtration and recommender technology.

"The social web will be powered by recommender systems".
Open Issues in Recommender Systems
John Riedl, Bilbao Recommenders School, 2006

The true power of the collective will be realized with the proper integration of social media, new universal discovery techniques, and associated detailed portable identity and personalization info. The result is a Social Web based on one degree of separation: all people and things are related to each other directly, with each such relationship differing only in type and strength. The following graphic is a representation of such a "one degree" circle of people relationships, but keep in mind that each person is also similarly related to all items, ideas, endeavors, etc. in the system as well.

Critical to this new paradigm are the new universal discovery techniques that I've hinted at previously. Current recommender systems, including collaborative filters, are too primitive and limited to accomplish the task. Instead, we have applied certain bioinformatics concepts to solve the puzzle of simulating the human preference engine without requiring "strong AI". This starts with a quick determination of a person's "core identity", that internal mechanism which is responsible for generating appreciation, and sifting through the chaos and making choices.

Determining that "core identity" is a critical breakthrough as it allows us to quantify the relationship (strength and type) between all people, and between all people and all other things in the system. It also can yield portable data that can be used to quantify such relationships between users and items from multiple data islands, and can even be used in mobile devices and in real-world activity. This discovery system involves no collaborative filtering, psychological testing or interpretation, statistical or stochastic methods, etc.

"But there is no go-to discovery engine - yet. Building a personalized discovery mechanism will mean tapping into all the manners of expression, categorization, and opinions that exist on the Web today. It's no easy feat, but if a company can pull it off and make the formula portable so it works on your mobile phone - well, such a tool could change not just marketing, but all of commerce."
The race to create a 'smart' Google
by Jeffrey M. O'Brien, writing for Fortune Magazine

In addition to the current benefits of the social web, the integration of these universal discovery techniques will allow:

• A brief one-page registration with no need for private information. Qualifies as 'Cold-Start' for people and also items, ideas, endeavors, etc.
• Immediate access to promising relationships of all types, i.e. universal recommendations. These relationships are the predicted interest and affinity between a person and all other people, music, movies, books, recreation, groups, products, services, ads, travel destinations, vocations, jobs, teams, politics, religion, ideas, websites, articles, news items, games, etc.
• Portable data that can be compared and relationships quantified. This portable data can be used between social and data islands, for mobile devices and in real-world activity.
• No language or cultural barriers: no folksonomy or semantic constraints.
• No need for existing relationships. Emphasis is on relationship discovery, though existing friends and contacts are revealing.
• No need to observe history of actions and choices. A one-page registration is enough to provide significantly more information, and better information, than collaborative filters can accumulate.
• The new system will act as a good friend who knows you well and delivers trusted recommendations of all types, both solicited and unsolicited.
• Reduced privacy concerns as personal or demographic data is unnecessary.
• Automatic person-level granularity. Each relationship has a strength and type.
• Universal recommendations allows for highly successful affiliations of all types, direct sales and downloads, and highly targeted advertising as the diverse business model.
• Ratio of discovery to effort is high. No need for constant messages, spam, requests, friend searches, etc.
• Discovery is filtration, so 'information overload' and the 'tyranny of choice' are greatly reduced.
• Enables highly personalized search engine functionality, news aggregation, and many other forms of person-level information filtration.
• Constant excitement of discovery, so no "what's next?" reaction. No limit to novelty and interest, little boredom. No feeling of wasted time.
• Highly useful and usable: the keys to success of any product or service.