I love snacks. But I understand they don’t always make for the healthiest diet.

I’d be interested in hearing arguments as to why short contributions imply lower quality.

The iPad/iPhone with its tiny applications as shown tat there can’t be much value in a small package.

Aditi, relevance feedback is also a possible way to guide expansion — basically allowing the user to help define the similarity metric. But I’m inclined to have that feedback be in the form of facet values rather than people, so that the communication is more transparent. “More like this person” doesn’t necessarily communicate what the user likes about a person.

So when you wrote about the difficulty of guided exploration for LinkedIn, I immediately thought -why not relevance feedback? With LinkedIn data, you have so many informative features for each person. You could easily have the recruiter select examples of the right kind of candidate, and have the system use relevance feedback to suggest more. If you did this within the framework of faceted navigation — continuing to show the distributions of companies, universities, and whatnot, the recruiter might themselves learn, “Oh, CMU and MIT seem to be really similar, maybe I can start there next time”.

It’s really fascinating how different two implementations of the same idea can be when developed in isolation from each other.

We incorporated the same guided expansion concept, but looked at it from the opposite angle. That being a bottom-up from the record, rather than testing alternate filter parameters. The algorithm that I came up with is (at a very high level):

1) Compute nearest-neighbors for every record in the full corpus. The distance (semblance) metrics are project specific. A huge number of weights can be taken into account. Index pointers from every record to the top X records above a fixed threshold on the computed distance value. Let’s call these the semblance indexes. This is an expensive but off-line computation.

2) When a user search is executed, create the result set as usual. Also create the faceted navigation results as usual. Let’s call these the core results.

3) Create a second set of results which is the union of the core result set, and all the records that are in the semblance indexes to the records in the result set.

4) Compute the faceted navigation results for the expanded set.

5) You can now compare the faceted navigation results from the core result set to the expanded results. There is a multitude of ways we’ve come up with for presenting that.

Step 3 is the expansion set. This was probably brought to mind by reading all of those fuzzy logic books in the 90s.

I think the net effect would be similar. Ya?

First, I wanted to place my screenshot on your blog, not post it as a hidden link on my website. I seem to recall in the past your explaining how to insert an image into a comment on TheNoisyChannel.com, but I couldn’t find your instructions in my e-mail, by searching your blog site, or via Google queries targeting your site’s content. Would you mind repeating those steps if this can be done?

Second, I really like the simplicity of checking or un-checking a check box to change or remove a criterion from a query. The older OO Query app displayed in my screenshot does not have this level of simplicity… something to think about if I ever decide to update and adapt that framework to work with distributed graphs, finish it, and make it available on the market.

Also, I initially was thinking that a similar decision tree-oriented tool might be helpful to you, the algorithm designer, in experimenting with your backwards faceted search framework – not end users (recruiters). My tool was designed for people such as FAA airspace monitoring personnel and it offered a lot of power and flexibility, albeit, at the cost of about a one hour learning curve. But this is off topic…

Thanks again, and if you would like some informal help on this project, I would be happy to offer some of my time. My thinking has evolved quite a bit since 2003 when I developed the OO Query tool and I might be able offer some ideas both in the UX and computational speed /storage areas.

The URL below contains a screenshot of my (old) OO Query Editor in which I mocked up three different variations on your query above with illustrative similarity metric comparison values shown between each of the query output result sets.

Would such a tool (plugged into LinkedIn’s graph store) be of use for evaluating and evolving candidate frameworks such as the framework in your posting, above?

Would an app similar to this be helpful to recruiters trying to formulate a candidate search?

(The actual GUI app uses reflection-derived metadata to guide the user through filling out each decision tree node with popups w/available attributes/predicates.)

Thanks again for writing the article.

Rick

PS: Here’s the URL: http://goo.gl/gZvcj

Thank you for the HCIR.

We at Elsevier Labs really enjoyed the outing and we look forward to this year’s event.

Congrats on your many technical adventures. It looked 2011 was a great year for your technical efforts to be rewarded.

Seriously, dbt is right. Good interviews are a conversation. Otherwise, it would be better to make them non-interactive tests.

What’s important to realize about these questions is that in an hour, you have time to make an attempt at an answer, get feedback, and improve your solution. It is a conversation, and not just a blank whiteboard, an interviewer in the corner tapping a ruler on the table every time you make a mistake, and a disappointing early trip home.

An alternative approach would be to put less emphasis on the accuracy of the interviewing process and treat the first few months as a trial period. Unfortunately, that’s not the cultural norm, so instead we try to squeeze all the risk out of the hiring process.

Anyway, if you’re able to write a compiler or OS, you should have no problem finding work you’re great at and enjoy.

However, the more I read the more I realize I’m a different kind of programmer than who is sought here. I don’t cite CLR by heart, solve real problems in real manner (i.e., including asking others, not to mention using books, Internet etc.) and generally not good at “coding”. I guess my skills aren’t very marketable, in this approach. Might be a better choice not to program for somebody else.

“[T]he stupider one is, the closer one is to reality. The stupid one is, the clearer one. Stupidity is brief and artless, while intelligence wriggles and hides itself. Intelligence is a knave, but stupidity is honest and straight forward.” — Dostoevsky (The Brothers Karamazov)

By the way, I’ve since moved on to less exciting coding problems that require less ingenuity and are more a test of basics (though not quite as elementary as fizzbuzz). I’m still surprised at how many candidates with strong resumes fail at these.

The idea is to build a set of rolling hashing function using the Rabin-Karp algorithm, for each word length in the dictionary, and the corresponding hash value for each word.

To segment the string, we loop over it once, updating the rolling hash values (for each length) and if we find a match in our set of hash values from the dictionary, we have a potential match. We still have to check the actual dictionary to confirm the match, avoiding false positives.

This design has the added advantage that the dictionary can be larger than what can fit into memory. We only need to store the hash values for each word in memory.

Subjectively coverage is great for small well-known domains like countries, U.S. politicians then drops to very good for films (probably even stronger than IMDB, particularly for non-English or older films) and continues on down to passable for books (much better than Wikipedia, but not as good as WorldCat), and so on. Sprinkled throughout are high value nuggets where someone has taken in interest in curation, but the real power is the breadth and connectivity.

I’d explain the accuracy measure a little differently than Bill did. My understanding is that the 95% is the confidence interval used to select the sample size. Given the total population of reconciled items, a large enough random sample is selected to give you a 95% confidence in your testing. That sample is then 100% human verified with multiple verifiers voting per item. If more than 1% of the sampled items fail human verification, the entire batch is rejected and sent back for more work.

The third rail in a railway is the exposed electrical conductor that carries high voltage power. Stepping on the high-voltage third rail usually results in electrocution. The use of the term in politics serves to emphasize the “shock” that results from raising the controversial idea, and the “political death” (or political suicide) that the unaware or provocative politician would encounter as a result.

I’m looking forward to Databus going open-source! It looks like it could be a great replacement for our java-pipeline sub-system.

Thanks Daniel!

-Carl

Anita: I’m pretty sure the Yahoo researchers captured statistics at the time of the tweets. And they used retweets, not clicks. I prefer measuring consumption using clicks, but I understand the pitfalls of the clickstream being polluted by bots or obfuscated for privacy reasons. And yes, the ability to measure any of these things depends on what data Twitter (and other social networks) makes available to researchers and practitioners.

As for clicks, I stopped using bit.ly to prevent details about my activist network being exposed in ways that may reveal too much information about them to hostile watchers. I can vouch for the fact that even links that attract no re-tweets can register hundreds of clicks. But it’s important to bear that in mind, not all those clicks are made by humans.

When this article was written, I think Twitter had not yet made t.co urls mandatory, and was allowing less restricted access to the search API. The changes to these and other aspects of the service since then (such as the near-elimination of RSS feeds) will have an impact on all anaylsis.

And for Karel: the way to address a tweet to or follow a TOPIC is still by using and following a hashtag, as far as I know.

Now THAT would be useful.

http://www.zdnet.com/blog/microsoft/microsoft-drops-dryad-puts-its-big-data-bets-on-hadoop/11226

OpenCorporates – 31 million corporate entities http://opencorporates.com

DERI Galway RDF extension – any SPARQL endpoint or RDF dump http://lab.linkeddata.deri.ie/2010/grefine-rdf-extension/

VIVO national scientific collaboration platform – http://sourceforge.net/apps/mediawiki/vivo/index.php?title=Extending_Google_Refine_for_VIVO

Talis Kasabi – any database published on the Kasabi platform http://kasabi.com/doc/api/reconciliation

To write a reconciliation service of your own, start here http://code.google.com/p/google-refine/wiki/ReconciliationServiceApi

I’d love to see how to get Google Refine to reconcile to arbitrary data sources. I apologize if I misrepresented that functionality. I use Google Refine myself, and like it, but I don’t see how to get it to join to data sources that were not specifically designed to do so (ie support SPARQL or “standard source” interface).

I do think being able to do so is something important and distinct. I look forward to finding out how to do so. I’ll dig around some more.

Thanks,

-Carl

The definitions of influence and influencers are, by the authors’ own admission, narrow and arbitrary. There are many ways one could define influence, even within the context of Twitter use. But I agree with the authors that these definitions have enough verisimilitude to be useful, and their simplicity facilitates quantitative analysis.

There is research that analyzes Twitter influence to demographics, e.g., “Understanding the Demographics of Twitter Users>“) as well as research on finding topic-sensitive influential Twitterrers. But as always there’s a trade-off between the realism of the model and the ability to use the model for analysis.

Tom, thanks for the correction — I’ve revised the post accordingly.

I’ll also post John’s slides if he sends them to me.

Doesn’t demographics play a vital role in tweet shelf life?

Don’t I need to find influential persons in fields related to my passions that I tweet about in order to gain the advantages of having a relationship with someone influential?

Is this a basis of “Identifying Influencers on Twitter” that is simply taken as a given and that I’ve missed?

Also, isn’t it a bit pointless to be “Identifying Influencers on Twitter” when that person certainly won’t retweet what I have to say, merely because the person is identified as an influencer? Dali Lama gets a lot of depth, but will the Dali Lama ever retweet my sales pitches for spam? I think not.

I understand why a company would want to find highly influential twitter users, but is merely saying “buy Coke” stated from someone identified as influential really going to get anywhere?

It seems demographics is completely ignored, unless, again, this is a given that is often times left unsaid.

Carl – Google Refine will reconcile against anything you want including the OpenCorporates database, any SPARQL endpoint, etc. (yes, I know your point was really just to pitch your product, but didn’t want the wrong impression left about Refine)

This blog post is an interesting continuation of our discussion about the process we (Bee) currently call Blosm. It is indeed very helpful, thanks. Freebase’s “Reconciling” is an important piece of technology. We call that process “suturing” within Blosm. If you don’t mind, I’d like to use Freebase as another data point to compare and contrast with our process. The main difference stems from us building special-purpose knowledge-bases instead of one central DB.

We build a dedicated FreeBase-like system (“knowledge-base”) tailored for the client’s topics, be-they products for an e-commerce site, or companies for a hedge fund. Features like Google Refine’s “reconcile to FreeBase”, puts FreeBase at the center of the universe, and thus limits the utility to only entities that are already in FreeBase. Instead we offer “reconcile to everything on the web (and usually lots of data feeds)”.

This flips the model, putting the client/seed data at the center. The technology necessary to quickly/affordably build a knowledge-base, using many more sources that are each much less manicured, is quite different. The resulting data is also very different, being much more special purpose in both content and structure.

Thanks,

-Carl

In the coming weeks, Google is likely to offer some tips for webmasters – either directly through Webmaster Tools and/or on their blog. It may pay to visit these sites more frequently than you normally would – at least for the next 4-6 weeks. [...]

Let’s see.. it’s been a while since I’ve done second grade arithmetic.. but 163 – 9 = 154 billion, which is a positive number. I think. A net benefit to society coming from the patent side.

There are two main arguments against technology patents. One has to do with the nature of innovation in the technology industry, which is different than inventions in other industries like pharmaceuticals…Technology products, and software especially, are never created from whole cloth. By their very nature, they build upon previous technologies and improve upon them.

Thanks especially to you, Daniel. You are an Endeca icon.

I am proud to be an Endeca (2004-2005) and Oracle (2000-2004) alumnus!

Unfortunately not every website is using this (although the above examples are very good) and I think this is where some websites will stand out.

Supermarkets with online shopping (including wine delivery) are particularly bad and some of them make it nearly impossibly to buy wine online, at least here in the UK.

Excellent presentation. Will you be presenting anything similar in the future?

Thad