Kikin Edge: Likable, Not Lovable

I recently received a gentle reminder that Kikin, a browser plugin that brings additional relevant content to Google search results has been updated and is accomplishing more than, as some blogger put it, filling in feature holes.

That blogger would be me, and the Kikin version I was reviewing at the time was duplicating the functions of Google’s left navigation column—the one that, um, brings you more relevant  content.

In February, Kikin revamped their Firefox plugin, it’s now called the Kikin Edge.

Time to take another look at it.

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

Freebase: Semantic Sandwich for Google

There actually was some significant news last week in the technoverse, and it didn’t involve another episode from Mark Zuckerberg’s reality show: on July 16, Google purchased Metaweb, the semantic database company and the force behind the freewheeling Freebase.

No doubt, the semantic web has entered into your own knowledgebase during the last year.

If it hasn’t, quick go to Google: enter empire state building height in the search box. Notice that the numeric height “1250 ft. ( 380 m.)” is highlighted in the search results. Google knew to answer this query with an actual number, instead of merely returning text snippets in which those search keywords were found. This flavor of artificial intelligence comes courtesy of an analysis of the knowledge space.

In a way, Google comprehended that “empire state building” is a structure, which has an attribute or property known as height, which itself has a numeric value associated with it measured in distance units.

Impressive.Continue reading