Month: July 2009

After the conference banquet at JFK Library and Museum, a few of us went to Bukowski for beers. At one point in the conversation, a friend of mine railed against computational advertising as a research topic. I didn’t quite have the nerve to reply that it was one of the topics I’d picked for the SIGIR 2009 Industry Track that would take place the following day.

Finding a speaker for this subject was relatively straightforward. I hadn’t yet recruited anyone from Yahoo!, and I knew that Yahoo! was the place to look for computational advertising experts. So I emailed Prabhakar Raghavan, and he suggested Vanja Josifovski. I’d never met Vanja or heard him speak, but a quick look at his publications and experience was more than enough to convince me. I was delighted when Vanja agreed to participate, presenting “Ad Retrieval – A New Frontier of Information Retrieval“.

I was even more delighted the actual presentation, which you can download here. Perhaps more than any of the other speakers, Vanja embodied the spirit of the Industry Track, which was to bring together the worlds of research and practice in information retrieval.

He started by making the case for textual advertising as an area worthy of study. He pointed out that, while advertising supports much of our access to search engines and online content, most users perceive ads as less relevant than the other content content they access. In other words, there is a significant opportunity for those in the advertising business to broadly improve the online user experience while making money.

He then proceeded to explain the anatomy of a textual ad. If you’re not familiar with the details, I encourage you to look at his presentation. But I’ll reproduce what I feel was his most important slide here, slide #15, titled “Ads as Information”:

• Treat the ads as documents in IR
  • [Ribeiro-Neto et al. SIGIR 2005] [Broder et al. SIGIR2007] [Broder et al. CIKM2008]
• Retrieve the ads by evaluating the query over the ad corpus
• Use multiple features of the query and the ad
• How does Ad retrieval relate to Web search?
  • Web search:
    • Large corpus
    • Reorder the pages that contain all the query terms
  • Ad retrieval:
    • Smaller corpus
    • Similarity search rather than conjunction of the query terms: recall in the first phase important

There’s a lot more to the talk, but hopefully that slide conveys how well Vanja posed ad retrieval as a distinctive information retrieval problem worthy of researchers’ attention.

Ironically, I’m not a big fan of advertising, and I see the dominance of the ad-supported model as a bug, rather than a feature, of our current online ecosystem. But I’m realistic enough to know that this dominance is a fact of life for the forseeable future, and I appreciate that better targeted advertising is a win/win for both advertisers and their audiences.

More importantly, I expect that efforts to improve advertising will result in advances in information retrieval that have broader applications. Vanja’s presentation advertised those benefits brilliantly.

After I secured Matt Cutts as a speaker for the SIGIR 2009 Industry Track, I suppose I became a bit cocky. I decided that I wanted another speaker who would not only be interesting, but also would have the star power to put the event on the map. One of my topics on my list was social media. So I decided, why not, I’ll try to get danah boyd.

This turned out to be no easy task. At the time, danah was on an email sabbatical (she was just wrapping up her dissertation at Berkeley). I’d actually been in touch with her several years ago, when Friendster was the only social network in town, and danah and Jeff Heer (whom I recently met at SIGMOD 2009) were working on visualizing it. I have my own history in network visualization, and I’d hoped to get access to their data. But no such luck. Still, discovering danah led me to read her master’s thesis (at the MIT Media Lab) on “Faceted Id/entity: Managing representation in a digital world“.

I actually given up on reaching her after a few weeks–reluctantly, I fell back to plan B. Fortuitously, however,  my plan B fell through, and I decided to try again. At long last I did reach her, only to discover I had to accomplish what I worried would be an even harder job: convincing her that her ethnographic research would be a good fit for an information retrieval audience. So I sent her this pitch:

I’d love to hear you talk about how the evolution of social media has changed the context of search. Going back to your master’s thesis, the collapsing of situational context in searchable archives has not only wreaked havoc on personal identity, but also made it difficult for searchers to meaningfully navigation those archives. And, since publication and search are flip sides of the same coin, we need to understand this dynamic better, especially as there is an increasing trend towards conducting public conversation.

It worked! The next thing I knew, she was on board to give a talk about “The Searchable Nature of Acts in Networked Publics”. As I expected, she was a fantastic speaker, and she had no problem engaging the audience. Rather than try to summarize her talk in detail, I refer you to a recent blog post of hers that covers very similar material. You can also read summaries of the actual talk by Mary McKenna at SemanticHacker and Daniele Quercia at MobBlog.

I saw the most salient theme of her talk as the need to intepret people’s behavior on online social networks (which, as she points out, come in many different flavors) in terms of their intentions. For example, teens on MySpace lie about their age, but they don’t see this behavior as deceptive behavior–after all, their online friends (who are the people for whom they publish their profiles) all know how old they are. In general, all of the information we provide online needs to be viewed through an appropriate contextual lens. Unfortunately (or perhaps fortunately for those who still are clinging to hopes of privacy), our data mining practices are a bit behind the curve.

The proliferation of user-generated content and public conversation–both of which amount to a democratization of publishing–is changing the ways we need to approach information retrieval in practice. danah’s talk left me with more questions than answers, but I appreciate her insightful snapshot of how people are using social networks today. And that snapshot only reinforced my certainty that I want to devote my life (or at least the next several years) to understanding and improving how people interact with information.

At last we arrive at the SIGIR 2009 Industry Track.Since I organized this track (which mainly involved coming up with a program and then actually producing the speakers), I’m not exactly an impartial observer. But hopefully the organizers of future industry tracks will benefit from my perspective as an organizer.

Last December (New Year’s Eve, to be precise), I started recruiting speakers. I started with a list of topics I wanted to see covered, and one of those topics was spam / adversarial information retrieval. My top two choices were Matt Cutts and Amit Singhal, both members of the Search Quality group at Google. I’d heard Amit speak before: he delivered one of the keynotes at ECIR 2008 (and inspired one of my first blog posts!). So I decided to aim for Matt Cutts, despite having no way to contact him (the head of Google’s Webspam team is understandably a bit protective of his personal email address). And, just two weeks later, I had Matt locked in to the program.

Matt was an incredible speaker, and he had the unenviable task of opening the Industry Track at 8:30 AM, the morning after the banquet. His title, “WebSpam and Adversarial IR: The Road Ahead”, gave him a fair amount of maneuvering room, and he used his 45 minutes to give the audience a peek into his world.

He opened the talk by inducing the audience to try to think like a spammer. He then game examples of social engineering attacks, to put us in a “black hat” mindset. He also pointed out the danger of punishing sites with spammy inlinks: people and companies would use this knowledge against their competitors / enemies (the practice has been called “Google bowling“).

He then moved on to examples of spam techniques. He showed examples of pages whose spaminess is only detectable by parsing JavaScript, something I wasn’t aware that Google could do (though apparently this has been public knowledge for a while). The theoretical computer scientist in me wonders about using random self-reducibility as obfuscation on steroids, but hopefully spammers aren’t quite that sophisticated yet!

He offered a common-sense framework for fighting spam: reduce the return on investment. Unfortunately, he sees a trend in spam where spammers are aiming for faster, higher payoffs by hacking sites and installing malware. Indeed, the democratizing effect of social media means that a lot more people have pages that can serve spam, including their Twitter and Facebook pages. He invited the information retrieval community to invest effort in learning how to automatically detect  that a page or server has been hacked.

My only quibble with the talk is that Matt did not discuss the inherent subjectivity of spam. Sure, there are many cases that are black and white, but ultimately spam (like relevance) is in the eye of the user. I’d love to see more use of techniques like attention bond mechanisms that accommodate a subjective definition of spam, e.g., “any email that you would rather have not received.”

But I quibble. Matt delivered an excellent talk to a packed audience, and it was a real privilege to have him kick off the Industry Track.

ps. You can also read Jeff Dalton’s notes on Matt’s presentation.

Albert-László Barabási is one of the biggest names in networking theory, up there with Jon Kleinberg and Duncan Watts. Since he was the only one of those three whom I hadn’t met, I was thrilled to discover that he was giving a keynote at SIGIR 2009, entitled “From Networks to Human Behavior“.

Much the keynote was a stump speech about the failure of Erdős–Rényi networks to explain real-world network phenomena and the surprising prevalence of scale-free networks that exhibit power law degree distributions (these are heavy-tailed distributions, unfortunately known to many as “long tail”). But it was an extremely compelling stump speech, and I found myself as mesmerized as those who were hearing this material for the first time. Barabási also pulled out some examples that I’d never seen before–in particular, that of a bot passing a sort of Turing test by emulating the bursty pattern of human-generated traffic. He also tried to explain competitive market dynamics (specifically Google vs. competitors) in terms of scale-free networks–an explanation that I thought was a bit of a stretch (the model included a fitness function vague enough for me to wonder if it was falsifiable), but nonetheless interesting. All in all, it was an excellent talk, especially given that it was a physicist talking about network theory to an audience of information retrieval researchers.

But what impressed me just as much was how Barabási handled questions. First, whatever information retrieval system his brain uses has incredible recall–he seemed to have a reference at his fingertips for any topic a questioner brought up. Second, he was incredibly warm, fielding questions that must have struck him as basic without showing even a hint of condescension.

Indeed, I asked a question that had plagued me for years. As Barabási explained, scale-free networks arise from preferential attachment–basically a pattern of network growth in which the rich (nodes) get richer (i.e., more edges). As a simple example, think of citation networks, like the World Wide Web. You’re more likely to cite (link to) pages that already have a lot of links, and hence there is a positive feedback loop. Yes, we’ll get to my question in a moment. Barabási further explained how scale-free networks are at once robust and vulnerable. Their connectedness is robust in the face of random failure: since the vast majority of nodes are those with small degree, a random failure is unlikely to take out a high-degree hub. But they are vulnerable to calculated attack, since removing the few hubs can have devastating consequences–an observation not lost on guerrillas or terrorists.

Yes, my question. I asked Barabási if the ubiquity of scale-free networks suggested that they had evolved because robustness in the face of random failure was a real-world fitness (in the Darwinian sense), and whether inter-species (or intra-species) competition would lead to the disappearance of those networks because of their vulnerability to calculated attack. In other words, were scale-free networks a transitional phase in our evolution as a global ecosystem?

Barabási’s answer surprised me: apparently one of his colleagues tried for two years to produce a compelling evolutionary argument for the ubiquity of scale-free networks, and failed to do so. Indeed, the best he could suggest is that scale-free networks, despite their obvious weaknesses, represent a good-enough solution from nature. Evolution satisfices.

I was stunned–both by the answer and by Barabási’s candor. I’d briefly worked on this problem myself as an amateur a few years ago (I wanted to reuse my graph drawing code!), but I assumed that the professionals had figured it all out. It’s humbling to know that nature and its tangled web of connections still holds deep mysteries that defy the intuition of its most astute observers.

ps. You can also read Jeff Dalton’s notes on the keynote.

At the two previous SIGIR conferences that I attended, the interactive search sessions were the most interesting, and this one was no exception. Ironically, even though many of us (myself included) feel that interaction is marginalized within the SIGIR conference and even the information retrieval research community, the few interaction talks at SIGIR consistently draw large audiences and lots of questions. Of course, it couldn’t hurt this time that Sue Dumais received the Salton Award for her contributions to HCIR.

This particular session consisted of three talks.

Like Jeff Dalton, I really liked Peter Bailey’s talk on “Predicting User Interests from Contextual Information“, work done in collaboration with Liwei Chen and my HCIR co-conspirator Ryen White. They analyze the predictive performance of contextual information sources (interaction, task, collection, social, historic) for different temporal durations (short, medium, large). Like Jeff, I’m a bit surprised that they used the Open Directory Project for their evaluation, but I do find their results compelling–if not entirely surprising. And here is irony for you: despite my at-best ambivalence toward advertising, I’d love to see their analysis applied to ad targeting, which strikes me as the best way to test their approach. You can also read a more complete summary from Max Van Kleek at the Haystack blog.

Diane Kelly presented the second paper, “A Comparison of Query and Term Suggestion Features for Interactive Searching” (unfortunately not available online yet), work done with UNC co-authors Karl Gyllstrom and Earl Bailey. David Karger and Gene Golovchinsky have already blogged about this talk, so rather than summarize I’ll add my personal reaction: I hope that their future work will consider query previews as a way of increasing the value of suggestion features. Her UNC colleague Gary Marchionini has made extensive use of such previews in the RAVE project, and I think they should join forces. I’m also hoping to present some of what we at Endeca have been doing in this area at HCIR ’09.

Robert Villa presented the third paper, “An Aspectual Interface for Supporting Complex Search Tasks“, a team effort with University of Glasgow co-authors Iván Cantador, Hideo Joho, and Joemon Jose. Again, David and Gene have already blogged great summaries. My micro-summary: nice research, but unfortunately the results are unfortunately inconclusive.

Given the evident interest in HCIR among SIGIR attendees, I hope the SIGIR community will make an effort to solicit and accept more papers in this area.

Sorry for the delay in postings. Not only was I super-busy the past week, but I had some connectivity challenges (both at SIGIR and at the apartment where I was staying) and mostly restricted my online activity to occasional tweets during talks. I meant to catch up on my blogging yesterday, but instead spent the day wine tasting in Long Island. But enough apologizing, I’m refreshed and ready to blog up a storm!

The second day of SIGIR (Tuesday) started straight off with research talks. I went to the web retrieval session, which consisted of two talks about anchor text and one about privacy-preserving link analysis.

“Building Enriched Document Representations using Aggregated Anchor Text“, by Don Metzler and colleagues at Yahoo Labs. They address the challenge of anchor text sparsity (the distribution of in-links for web pages follows a power law) by enriching document representation through aggregation of anchor text along the web graph. Their technique is intuitive, and the authors demonstrate statistically significant improvements in retrieval effectiveness. Unfortunately, their results are not repeatable, since used a proprietary test collection to obtain them.

The second talk of the session, “Using Anchor Texts with Their Hyperlink Structure for Web Search“, was by a group of authors from Microsoft Research Asia. They address the opposite problem of the previous paper: how to handle too much, rather than too little, anchor text. Specifically, they model dependence among multiple anchor texts associated with the same target document. Like the Yahoo folks, they demonstrate statistically significant results on a proprietary test collection.

The third talk, “Link Analysis for Private Weighted Graphs” (ACM DL subscribers only) by Jun Sakuma (University of Tsukuba) and Shigenobu Kobayashi (Tokyo Institute of Technology), was a bit of an outlier, if one can call a paper in a three-paper session an outlier. The authors offer privacy-preserving expansions of PageRank and HITS, the best-known link analysis methods associated with relevance and authority in web search. I’ve noticed an increasing number of papers like these that mix cryptography with information retrieval or database concerns. One of my frustrations in reading such papers is that I always suspect that people are re-inventing wheels because so few people are able to keep up with research in multiple disciplines.

Then I had the coffee break to solve my own research problem: how to fill the 11:30 slot in the Wednesday Industry Track, since a speaker called in sick that morning. When I walked by the Bing table, I saw Jan Pedersen (Chief Scientist for Core Search at Microsoft), and I begged him to help me out. I must have been a persuasive supplicant, because he procured me Nick Craswell, an applied researcher who works on Bing. Out of gratitude for this 11th-hour favor, I wore a Bing t-shirt all day yesterday as I went wine-tasting. Bing drinking, not binge drinking!

Anyway, that urgent problem resolved, I went back to enjoying the conference. For the second morning session, I went to the vertical search session.

As it turns out, that session kicked off the with SIGIR Best Paper winner: “Sources of Evidence for Vertical Selection” by Jaime Arguello (CMU), Fernando Diaz (Yahoo), Jamie Callan (CMU), and Jean-François Crespo (Yahoo). The authors do a lot of things I like: they apply query clarity as a performance predictor, and they bootstrap on an external collection (specifically Wikipedia). The test collection they use for evaluation is proprietary, but that seems to be the price (at least today) of doing this kind of work.

The second talk of the session was by a subset of the previous paper’s authors: “Adaptation of Offline Vertical Selection Predictions in the Presence of User Feedback” by Fernando Diaz and Jaime Arguello. The authors creatively used simulation to evalaute their approach. They did a nice job, but I have to admit I’m skeptical of results about feedback that aren’t based on user studies.

Unfortunately, I missed the third talk of the session because I had to play organizer. But I must have earned some good karma, because I got to enjoy a delightful lunch with Marti Hearst and David Grossman.

Stay tuned for more posts about the interactive search session, the keynote by Albert-László Barabási, the banquet at the JFK Presidential Library and Museum, and of course the Industry Track.

SIGIR ’09 is in full swing!

I arrived on Sunday evening, and the reception was like Cheers (“where everyone knows your name“)–only that, at least in my case, I was meeting many people face-to-face for the first time in years, and in some cases for the first time, period! I reconnected with some of the SIGIR regulars whom I’d missed last year (Singapore was a bit far for me), finally met my editor, Diane Cerra from Morgan & Claypool, and even ran into someone who is evaluating my company’s technology. And that was just Day 0.

Day 1 started bright and early with the 7:00 am newcomer’s breakfast, which brings together newcomers and “old hands”. I believe my role as organizer qualified my as an “old hand”, even though this is only my third SIGIR. Which might explain why Justin Zobel, a real old hand (and one of this year’s program chairs) joined my table. Of course, he hadn’t read my post about his recent SIGIR Forum essay, so we chatted a bit about recall. Not surprisingly, we mostly agreed, and I have to give credit to the essay for provoking that and other good discussions today.

Then the conference started in earnest, with Liz Liddy bestowing  the Salton Award to Susan Dumais. In the tradition of the award, Sue delivered a keynote recounting her personal journey through the space of information retrieval. I was thrilled that her recognition called out her working to bring together information retrieval and human-computer interaction. Of course some of us were ahead of the curve by recruiting her as the keynote for HCIR ’08. 🙂 Of course, I asked her a question about why transparency, which she called out as a reason that users in her Stuff I’ve Seen work preferred to explicitly sort results by date rather than accept the systems best-first relevance ranking, was so absent in web search. Her answer was interesting: she feels that transparency is most useful for re-finding, and least useful for discovery. I’m not sure I agree with that explanation, but I’ll at least think about it a bit before I commit to disagreeing with it.

Some coffee, and then off to the first session of research papers. The presentation that stood out for me in this session was “Refined Experts“, presented by Paul Bennett. The paper offers a nice technique for improve on hierarchical classification (by addressing the problems of error propagation through the hierarchy and the inherent non-linearity of hierarchies), and Paul is an outstanding presenter.

Then Diane Cerra and Gary Marchionini took the Morgan & Claypool authors (and a few authors-to-be) to lunch at Brasserie Jo. Great food, and even better company. My only regret is that I missed one of the talks in the first session after lunch, “A Statistical Comparison of Tag and Query Logs“. I did like David Carmel‘s talk on “Enhancing Cluster Labeling Using Wikipedia” in that same session, though I’ll need to do some homework to figure out what distinguishes it from other work in this area, such as an ICDE 2008 paper by Wisam Dakka and Panos Ipeirotis on “Automatic Extraction of Useful Facet Hierarchies from Text Databases“.

In the following session, I attended a couple of the efficiency talks. The talks were well presented, but in both cases I wondered if they were addressing the right problems. I’ve felt this way before at SIGIR efficiency talks, so perhaps my tastes are just idiosyncratic.

Then came the poster / demo reception. Even with three hours, there was far too much to take in–and of course that session is as much about networking as it is about the posters and demos. I enjoyed the three hours, but I’ll have to go back to the proceedings to learn more about what I saw–and what I missed.

Finally, I wrapped up by leading a crew to Tapeo for dinner–apparently a popular choice for attendees, since another table of 6 arrived shortly afterward. It was a nice cap to a fantastic but exhausting day.

I can’t promise I’ll keep this up daily, but I will blog about the rest of the conference when I have the chance. Meanwhile, here are some other folks blogging about SIGIR ’09:

• David Karger is live blogging on the Haystack blog.
• Jon Elsas is blogging and posting some pictures.
• Jeff Dalton posted detailed notes about Sue’s keynote.
• Mary McKenna is blogging at the SemanticHacker blog.
• John Battelle and Gene Golovchinsky couldn’t attend, but have both blogged about the conference.

And follow on Twitter. The preferred hashtag is #sigir09, but I follow sigir OR sigir09 to be safe.

It’s not everyday that you see an essay in SIGIR Forum entitled “Against Recall”. Well, to be fair, the full title is “Against Recall: Is it Persistence, Cardinality, Density, Coverage, or Totality?” In it Justin Zobel, Alistair Moffat, and Laurence Park, all researchers at the University of Melbourne, conclude that “the use of recall as a measure of the effectiveness of ranked querying is indefensible.”

It’s a well-written and well-argued essay, and I think the authors at least have it half-right. I agree with their claim that, while precision dominates quantitative analysis of search effectiveness in the research literature, the expressed concerns about recall tend to be more qualitative. Part of the problem, as they note, is that recall is much harder to evaluate than precision (assuming the Cranfield perspective that the relevance of a document to a query is objective).

The authors propose a variety of alternate measures that, in their view, are more useful than recall and are actually what authors really mean when they allude to recall. The most interesting of these, in my view, is what they call “totality”. Indeed, I thought the authors were addressing me personally when they wrote:

It is usual for certain “high recall applications” to be cited to rebut suggestions that recall is of little importance. Examples that are routinely given include searching for precedents in legal cases; searching for medical research papers with results that relate to a particular question arising in clinical practice; and searching to recover a set of previously observed documents.

Yup, I’m listening. They continue:

While we agree that these are plausible search tasks, we dispute that they are ones in which recall provides an appropriate measurement scale. We argue that what distinguishes these scenarios is that the retrieval requirement is binary: the user seeks total recall, and will be (quite probably) equally dissatisfied by any approach that leaves any documents unfound. In such a situation, obtaining (say) 90% recall rather than a mere 80% recall is of no comfort to the searcher, since it is the unretrieved documents that are of greatest concern to them, rather than the retrieved ones.

Whoa there, that’s quite a leap! Like total precision, total recall is certainly an aspiration (and a great Arnie flick), but not a requirement. There are lots of information retreival applications where false negatives matter a lot to us a lot more than false positives–notably in medicine, intelligence, and law. But often what is binary for us is not whether we find all of the “relevant” documents for each individual query–and here I use the scare quotes to assert the subjectivity and malleability of relevance–but rather whether or not we ultimately resolve our overall information need.

Let me use a concrete example from my own personal experience. When my wife was pregnant, she had gestational diabetes. She treated it through diet, and up through week 36 or so things were fine (modulo the trauma of a Halloween without candy). And then one of her doctors made an off-hand allusion to the risk of shoulder dystocia. She came home and told me this, and of course we spent the next several hours online trying to learn more. We had a very specific question: should we opt for a Cesarean section?

I can tell you that no search engine I used was particuarly helpful in making this decision. I was hoping there might be analysis out there comparing the risks of shoulder dystocia with the risks associated with a Cesarean, particularly for women who have gestational diabetes. I couldn’t find any. But worse, I had no idea if there was helpful information out there, and I had no idea when to stop looking. Ultimately we took our chances, and everything turned out great–no shoulder dystocia, no Cesarean, and a beautiful, healthy baby and mother. But it would have been nice to feel that our decision was informed, rather than a nerve-wracking coin toss.

Let’s abstract from this concrete example and consider what I characterize as the information availability problem, where the information seeker faces uncertainty as to whether the information of interest is available at all. The natural evaluation measures associated with information availability are the correctness of the outcome (does the user correctly conclude whether the information of interest is available?); efficiency, i.e., the user’s time or labor expenditure; and the user’s confidence in the outcome.

It’s worth noting that recall is not on the list. But neither is precision. We’re trying to measure the effectiveness of information seeking at a task level, not a query level. But it’s pretty easy to see how precision and recall fit into this scenario. Precision at a query level helps most with improving effeciency at a task level, while recall helps improve correctness of outcome. Finally, perceived recall should help inspire user confidence in the outcome.

To circle back to the essay, I said that the authors were at least half right. They criticize the usefulness of recall for measuring ranked retrieval, and I think they have a point there–ranked retrieval inherently is more about precision than recall. Recall is much more useful as a set retrieval measure. The authors also note that “the idea that a single search will be used to find all relevant documents is simplistic.”

Indeed, I’d go beyond the authors and assert, straight from the HCIR gospel, that the idea that a single search will be used to fully address an information seeking problem is simplistic. But that assumption is the rut where most information retrieval research is stuck. The authors make legitimate points about the problems of recall as a measure, but I think they are missing the big picture. They do cite Tefko Saracevic; perhaps they should look more at his communication-based framework for thinking about relevance in the information seeking process.

I’m glad I have a Twitter alert for “faceted search”, since it alerted me (via @getzsch) to a post in TechCrunch announcing that LinkedIn now has a People Search beta that offers faceted search. I can disclose now that I known about this project for a while–they’d reached out to me after I offered a lukewarm review of their search–but I was asked to be discreet about that knowledge.

In any case, I wish I’d known about the beta launch earlier today, when I was looking for Boston-area colleagues to help me publicize the SIGIR Industry Track! The current interface is much more supportive of exploration.

It’s a nice implementation. The interface lets you refine the text search results by location, relationship (1st degree, 2nd degree, group, and other), industry, current / past company, and school. For a facet with a large number of values, like company, the interface only displays the top 10 values, and then lets you use type-ahead to refine by other companies. Unfortunately, the type-ahead was a bit buggy for me–but hey, it is a beta.

The application is fairly responsive, even for my search for “software”, which returns 2.4M results, 120K of which are 2nd-degree connections. Other than at Endeca, I haven’t seen anyone else mix faceted search with social networks, and LinkedIn has done a nice job of it.

So, if anyone from LinkedIn is reading this, congratulations and welcome to the wonderful world of faceted search. Count me a delighted customer. I hope my enthusiasm today makes up for my past criticism.