GetGlue: Platforms, Brand Ambassadors, and Puccini

I’ve been writing lately on rating and suggestion services and their underlying data prediction technologies, which are fascinating.

What about those users (like me) who don’t completely trust the algorithmically generated suggestions that are proffered?

They can instead lose themselves in the stream of likes and comments that are displayed in the standard “recent activity” box found on the home pages of these sites. It’s a direct way to pick up ideas on movies, books, food, TV shows, and lizards.

I made up the part about lizards, but the point is that with social rating sites, anything in this world can be judged as good or bad and then become a part of the intimate information flow for the rest of humanity to see.

For example, GetGlue, the recommendation service I’ve been referring to in my posts, has an Android (and iPhone) app that lets the crowd comment on what they’re currently reading, watching, listening, or thinking. It’s really a check-in service—Foursquare without being tied to a specific physical place

With my new Yixin Android tablet now on my coffee table, I’ve become another gadget-owning media critic. Continue reading

Do I Need a Web Recommendation Service?

Xydo is a recommendation startup I first discovered at Hoboken Tech Meetup. Since then I’ve partially trained GetGlue and Hunch to respond to my tastes (not successfully), perused’s recommendation app for filtering feeds, and gauged Google’s own Prediction APIs and Set suggestion tools (pretty good stuff).

So when I received the beta invite from Xydo, I was almost at the beginnings of an existential crisis: do I really need a web site to show me other URLs to look at? After all, I was heavily reliant on Google Reader to bring the feeds I like to my attention. I wasn’t sure whether I required additional content advice.

I would want Xydo and other such sites to be my web magazine 2.0, bringing both content that I absolutely need yet also uncannily anticipate what I may want.Continue reading’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

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

Suggestion Software: Perfect match for idle CPU cycles.

I recently tried a silicon oracle called GetGlue.  I’m genuinely impressed at how racks of CPUs can quickly navigate through an enormous knowledge graph and grab a suggestion node that matches some characteristic of my preferences. Great searching capabilities, but I’m less than excited about the results.

Like other recommendation software (say, Hunch), you register preferences by initially rating a sample list of movies, books, TV shows, and music. I told GetGlue that I liked The Breakfast Club, Goodbye Columbus, The Great Gatsby, and Nina Simone.  GetGlue quickly responded with a list of predicable tips: lots of Philip Roth novels, Catcher in the Rye, American Beauty, and Sara Vaughn.

I suppose if I had lived in vault, then some of these suggestions would be novel. One quibble for the GetGlue crew: how about adding a “like, but already know about” classification.

On the plus side, GetGlue deserves credit for bringing swing band leader Jimmie Lunceford, who I hadn’t heard known about, to my attention.  Thanks.Continue reading