An API by itself can add great value for both a site visitor and a site operator. An API by itself can offer a huge return-on-investment for both user and operator, too. Yet integrating one API with another API, a so-called API mash-up, can offer an order of magnitude more for both user and operator.

Picture, if you will, a website dedicated to restaurants. Some of these websites cover the world, some a nation, some a specific region such as a city or a county. While other users can rate specific restaurants, those ratings are very limited. This is where a predictive relevancy engine such as CRITEO can help add value to those ratings — it allows various users to see what else they might like or dislike, and allow the to cultivate a better relevancy by continuously adding to their ratings. But for some, that might not be enough. A “I like it” or “I don’t like it” rating is great, but what more could you offer?

There are incredible APIs out there for user-interactivity: Google Earth’s API is awesome since it can allow a site operator to limit a user’s view of their site to a specific region, maybe a 10 mile radius to where the user lives. The relevancy predictor could return a list of dozens of restaurants the user might like (or dislike, or whatever the user filters by), but a connection to an external API such as Google Earth or Google Maps can allow the user to limit their list from a huge international list of restaurants to one that lets them focus on what is important to them. A network of sites dedicated to various regions can co-exist within the same relevancy prediction, allowing users to travel to other areas before a vacation to see what they might like.

I might run a review site on my region (Lake County, Illinois), but I might want to work with hundreds of other sites that cover specific counties, too. If we share the same prediction engine through a cooperative network, my regular users could allow their past decisions to reflect what they might like elsewhere. Say a Chicago resident wants to go to Las Vegas, and wants a good review of Italian restaurants. Right now they might go to another site than mine, but their information there might be irrelevant to their likes and dislikes. By combining a variety of APIs real-time, and allowing a co-op of individual sites, a relevancy engine could really flourish by taking into account the current user’s ratings, and diagnose other users within the network and their likes and dislikes. A user can make a decision based on the multiple-API predictions, and after actually visiting the restaurant, their review can increase the relevancy for thousands or even millions of other users who normally have no connection to one-another.

This entry was posted on Tuesday, December 5th, 2006 at 10:00 am and is filed under Possible Markets. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.