’s P3 Platform

I was finally able to spend quality time with the Reader, an app designed to show some of the capabilities of the underlying platform, called P3, which is currently in beta. To be clear, unlike many other players in the recommendation patch (GetGlue, Xydo, Hunch, etc.), this NYC-based startup is not in the business of providing a direct service to users.

Instead they give access to their cloud-based recommendation server through a set of RESTful APIs. The Reader app is just a demonstration of what can be done with their technology.

So what can be done?

After reading through the P3 reference documents and interacting with the Reader, you quickly see that P3’s aim is to reproduce formerly expensive, proprietary technology mastered by a few players (Netflix, Amazon) for businesses in general— most likely, those in the small-to-medium bins.

It’s another Nick Carr moment for me, in which technology has turned a previously mysterious application, recommendation algorithms in this case, into something closer to an appliance meant for wider usage. Continue reading

Google’s Pretty Good Recommendation Service

I’m still near the starting point in my travels through recommendation services and their underlying algorithms. It’s always a great help therefore to meet a more experienced knowledge hiker returning from the other direction who can offer a better sense of the terrain ahead.

We received a comment from Sachin Kamdar, founder of recommendation startup, in response to a post last week on Freebase and knowledge networks that gave us just such an insight.

Kamdar’s point is that you can get pretty far—but not all the way, of course—by extracting patterns from datasets. Even a simple pattern matching algorithm can be useful., by the way, employs both data mining techniques and language processing in generating its recommendations.

So how far can you go with pattern matching and a little semantic analysis?

To find out we tried Google Sets.Continue reading

Knowledge-based Recommendations

Over the last few months, recommendation startups have sprouted up—getglue, Hunch, Foodspotting,, Miso, Xydo (in beta), Bubbalon, etc.—to offer suggestions about restaurants, books, web sites, or just about anything in this world.

If you add in Facebook (with its like button, and lots of 3rd-party rating apps ), Amazon, and NetFlix, there’s enough of a universe to merit a service that rates and recommends recommendation services. There’s a startup, no doubt, working this out.

All share the idea that there’s wisdom in the crowd, and to various extents use stats about the mob to algorithmically classify tastes—clustering, nearest neighbor,decision trees—and then generate suggestions. There’s a nice summary of these collaborative filtering techniques in the reference section below.

What about a more conventional, common-sense approach that derives wisdom from actual knowledge of the subject?Continue reading

Civilization Gets More Organized

An internet pundit wrote a much linked to piece of punditry about how complexity overwhelmed the administrative powers of a few past civilizations, thereby leading to their eventual demise. Last night at a NY Tech Meetup I was feeling incredibly optimistic about the prospects of our own  society.

What’s one of the most vexing problem faced by many Manhattanites? Finding a cab would probably come in pretty close to the top—finding a cab in the rain, a little higher.

So I was starry eyed at a demo of a new iPhone app (which has received media attention recently) called CabSense.

Using GPS data collected by the  New York City Taxi and Limousine Commission, AI-machine learning researchers were able to discern patterns in what I  always thought was a random walk.  The result was a mobile  app  that taps into this dataset  and reports back a nearby street corner where you are likeliest to get a cab.

CabsSense (brought to you by start-up Sense Networks) was one of several demos I witnessed last night that in my mind were all connected by a deeper theme.Continue reading