Introduction

Affinax is a novel universal targeting technology that anticipates a person’s preferences in any domain of life, with much greater speed, without popularity bias, and using far less input about users and items than existing technologies require. Much of commerce and discovery on the internet is currently powered by relatively primitive targeting technologies. With its substantial advancements, Affinax should be able to make a significant impact on internet advertising and discovery, information filtration, and online and mobile commerce. Join us.


Dec 18, 2007

The Future of the Internet

I found an interesting analysis of existing applications and ideas about the future of the internet on the CNN website by Jeffrey M. O'Brien, writing for Fortune Magazine. He says:

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 effect of recommender systems will be one of the most important changes in the next decade," says University of Minnesota computer science professor John Riedl, who built one of the first recommendation engines in the mid-1990s. "The social web is going to be driven by these systems."

I see a growing interest in recommender systems everywhere I look, and I tend to agree with the above that the next big thing likely involves personalized discovery and recommendations. It seems clear that in order to improve the current state of the internet information overload must be reduced as well as the burden of choice - this will require advancements in recommender technology. One can also assume that the internet will continue and expand the benefits of "Web 2.0" and the collaborative internet. These benefits are, like most products and services, increased ease of use and increased usefulness.

Some feel that the "Semantic Web" or similar technologies are the future. Essentially this involves "teaching the machine" about content through the creation of universal, machine readable formats. Those critical of the limited scope of "folksonomies" champion a more standardized approach, thinking that if only people could be compelled to be disciplined in their approach toward organization of content, that some kind of utopia would emerge. There are as many critics of the Semantic Web as there are of folksonomy, and the fact that people have been talking about schemas and such for years, seems to indicate that - short of some radically new approach - there will be no utopia there. Interestingly, it may be a phenomenon called "social network fatigue" that finally forces services to adopt a standardized portable social/data/identity ontology.

The new revolution must also bring people together in much better ways than those that employ the "degrees of separation" format. Online Social Networks are extremely popular because they allow people to keep in virtual contact with their existing friends and contacts, and they can facilitate personal expression and PR. Unfortunately, social nets are not really social, and the network - as degrees of separation - serves mostly to separate. Registering for one of these sites is a rather cold experience unless you already have tons of friends and contacts already registered. Otherwise you have to spam your friends and nag them to register - something that they may not appreciate. The burden of registration and establishing relationships is compounded as more and more social nets are created, each of them requiring the time consuming input of the same personal data.

The next big thing must facilitate discovery of new people based only on similarities of their core identities, rather than focusing only on a user's primary social network. Two identical twins, separated at birth and raised in countries on opposite sides of the planet, speaking different languages, should be able to find each other. Bringing people together must include the separate objectives of: romance, friendship, business relationships, work team formation, roommates, travel and recreation buddies, etc.

In addition to people finding other people of similar core identity, the next big thing will need to do the same for non-human entities, like music, movies, TV, books, news, web pages, articles, games, products, services, vocations, jobs, travel destinations, politics, religion, advertising, or any other activity, enterprise, product, service, endeavor, idea, belief, passion or item.

In order for such matching to be accurate and satisfying for the user, there must be low false positives ("trust busters"), low false negatives ("missed opportunities") and sufficient true positives ("new favorites"). No current recommendation or recommender system comes close to even approaching this goal. Matching cannot be based on identical answers or choices, as there can never be enough questions to encompass all of human individuality. More importantly, people's existing interests in music, books, movies, etc. is not strictly linked to their core identities, but rather often more on prevailing cultural and social influences.

In our short lives, we are unlikely to ever find the people and things that we would most enjoy and appreciate. This is unfortunate.

The new internet revolution must match people to other people and things based solely on that core identity. It must do it with only a brief registration, where, upon registering, you are immediately presented with your ultimate best friends and soulmates, ideal potential business partners, as well as new favorites in every area of life. It should do it without language barriers (i.e. folksonomy or schemas), without the need for a large staff (i.e. Pandora), without a tedious registration (i.e. eHarmony), without having to observe your history (i.e. Amazon.com, Netflix, Last.fm, etc.), without requiring existing friends or contacts (i.e. Facebook, MySpace, LinkedIn, etc.), and it must be completely free for users but able to convert many of those recommendations into revenue.

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