Month: July 2008

Today’s big tech news is the launch of Cuil, the latest challenger to Google’s hegemony in Web search. Given the impressive team of Xooglers that put it together, I had high expectations for the launch.

My overall reaction: not bad, but not good enough to take seriously as a challenge to Google. They may be “The World’s Biggest Search Engine” based on the number of pages indexed, but they return zero results for a number of queries where Google does just fine, including noisy channel blog (compare to Google). But I’m not taking it personally–after all, their own site doesn’t show up when you search for their name (again, compare to Google). As for their interface features (column display, explore by category, query suggestions), they’re fine, but neither the concepts nor the quality of their implementation strike me as revolutionary.

Perhaps I’m expecting too much on day 1. But they’re not just trying to beat Gigablast; they’re trying to beat Google, and they surely expected to get lots of critical attention the moment they launched. Regardless of the improvements they’ve made in indexing, they clearly need to do more work on their crawler. It’s hard to judge the quality of results when it’s clear that at least some of the problem is that the most relevant documents simply aren’t in their index. I’m also surprised to not see Wikipedia documents showing up much for my searches–particularly for searches when I’m quite sure the most relevant document is in Wikipedia. Again, it’s hard to tell if this is an indexing or results quality issue.

I wish them luck–I speak for many in my desire to see Google face worthy competition in web search.

Now that SIGIR ’08 is over, I hope to see more folks blogging about it. I’m jealous of everyone who had the opportunity to attend, not only because of the culinary delights of Singapore, but because the program seems to reflect an increasing interest of the academic community in real-world IR problems.

Some notes from looking over the proceedings:

• Of the 27 paper sessions, 2 include the word “user” in their titles, 2 include the word “social”, 2 focus on Query Analysis & Models, and 1 is about exploratory search. Compared to the last few SIGIR conferences, this is a significant increase in focus on users and interaction.
• A paper on whether test collections predict users’ effectiveness offers an admirable defense of the Cranfield paradigm, much along the lines I’ve been advocating.
• A nice paper from Microsoft Research looks at the problem of whether to personalize results for a query, recognizing that not all queries benefit from personalization. This approach may well be able to reap the benefits of personalization while avoiding much of its harm.
• Two papers on tag prediction: Real-time Automatic Tag Recommendation (ACM Digital Library subscription required) and Social Tag Prediction. Semi-automated tagging tools are one of the best ways to leverage the best of both human and machine capabilities.

And I haven’t even gotten to the posters! I’m sad to see that they dropped the industry day, but perhaps they’ll bring it back next year in Boston.

Just a few days ago, I was commenting on a New York Times article about Wikipedia’s new approval system that the biggest problem with Wikipedia is anonymous authorship. By synchronous coincidence, Google unveiled Knol today, which is something of a cross between Wikipedia and Squidoo. It’s most salient feature is that each entry will have a clearly identified author. They even allow authors to verify their identities using credit cards or phone directories.

It’s a nice idea, since anonymous authorship is a a major factor in the adversarial nature of information retrieval on the web. Not only does the accountability of authorship inhibit vandalism and edit wars, but it also allows readers to decide for themselves whom to trust–at least to the extent that readers are able and willing to obtain reliable information about the authors. Without question, they are addressing Wikipedia’s biggest weakness.

But it’s too little, too late. Wikipedia is already there. And, despite complaints about its inaccuracy and bias, Wikipedia is a fantastic, highly utilized resource. The only way I see for Knol to supplant Wikipedia in reasonable time frame is through a massive cut-and-paste to make up for the huge difference in content.

Interestingly, Wikipedia does not seem to place any onerous restrictions on verbatim copying. However, unless a single author is 100% responsible for authoring a Wikipedia entry, it isn’t clear that anyone can simply copy the entry into Knol.

I know that it’s dangerous to bet against Google. But I’m really skeptical about this latest effort. It’s a pity, because I think their emphasis is the right one. But for once I wish they’d been a bit more humble and accepted that they aren’t going to build a better Wikipedia from scratch.

As regular readers have surely noticed by now, I’ve been on a bit of a behavioral psychology kick lately. Some of this reflects long-standing personal interest and my latest reading. But I also feel increasingly concerned that researchers in information seeking–especially those working on tools–have neglected the impact of cognitive bias.

For those who are unfamiliar with last few decades of research in this field, I highly recommend a recent lecture by behavioral economist Dan Ariely on predictable irrationality. Not only is he a very informative and entertaining speaker, but he chooses very concrete and credible examples, starting with his contemplating how we experience pain based on his own experience of suffering
third-degree burns over 70 percent of his body. I promise you, the lecture is an hour well spent, and the time will fly by.

A running theme of through this and my other posts on cognitive bias is that the way a information is presented to us has dramatic effects on how we interpret that information.

This is great news for anyone who wants to manipulate people. In fact, I once asked Dan about the relative importance of people’s inherent preferences vs. those induced by presentation on retail web sites, and he all but dismissed the former (i.e., you can sell ice cubes to Eskimos, if you can manipulate their cognitive biases appropriately). But it’s sobering news for those of us who want to empower user to evaluate information objectively to support decision making.

In Psychology of Intelligence Analysis, Richards Heuer advocates that we quantify expressions of uncertainty: “To avoid ambiguity, insert an odds ratio or probability range in parentheses after expressions of uncertainty in key judgments.”

His suggestion reminds me of my pet peeve about the unquantified notion of reasonable doubt in the American justice system. I’ve always wanted (but never had the opportunity) to ask a judge what probability of innocence constitutes a reasonable doubt.

Unfortunately, as Heuer himself notes elsewhere in his book, we human beings are really bad at estimating probabilities. I suspect (with a confidence of 90 to 95%) that quantifying our uncertainties as probability ranges will only suggest a false sense of precision.

So, what can we do to better communicate uncertainty? Here are a couple of thoughts:

• We can calibrate estimates based on past performance. It’s unclear what will happen if people realize that their estimates are being translated, but, at worst, it feels like good fodder for research in judgment and decision making.
• We can ask people to express relative probability judgments. While these are also susceptible to bias, at least they don’t demand as much precision. And we can always vary the framing of questions to try to factor out the cognitive biases they induce.

Also, we talk about uncertainty, it is important that we distinguish between aleatory and epistemic uncertainty.

When I flip a coin, I am certain it has a 50% chance of landing heads, because I know the probability distribution of the event space. This is aleatory uncertainty, and forms the basis of probability and statistics.

But when I reason about less contrived uncertain events, such as estimating the likelihood that my bank will collapse this year, the challenge is my ignorance of the probability distribution. This is epistemic uncertainty, and it’s a lot messier.

If you’d like to learn more about aleatory and existential uncertainty, I recommend Nicholas Nassim Taleb’s Fooled by Randomness (which is a better read than his better-known Black Swan).

In summary, we have to accept the bad news that the real world is messy. As a mathematician and computer scientist, I’ve learned to pursue theoretical rigor as an ideal. Like me, you may find it very disconcerting to not be able to treat all real-world uncertainty in terms of probability spaces. Tell it to the judge!

Today’s New York Times has an article by John Markoff called On a Small Screen, Just the Salient Stuff. It argues that the design constraints of the iPhone (and of mobile devices in general) lead to an improved user experience, since site designers do a better job of focusing on the information that users will find relevant.

Of course, on a blog entitled The Noisy Channel, I can’t help praising approaches that strive to improve the signal-to-noise ratio in information seeking applications. And I’m glad to see them quoting Ben Shneiderman, a colleague of mine at the University of Maryland who has spent much of his career focusing on HCIR issues.

Still, I think they could have taken the idea much further. Their discussion of more efficient or ergonomic use of real estate boils down to stripping extraneous content (a good idea, but hardly novel), and making sites vertically oriented (i.e., no horizontal scrolling). They don’t consider the question of what information is best to present in the limited space–which, in my mind, is the most important question to consider as we optimize interaction. Indeed, many of the questions raised by small screens also apply to other interfaces, such as voice.

Perhaps I am asking too much to expect them to call out the extreme inefficiency of ranked lists, compared to summarization-oriented approaches. Certainly the mobile space opens great opportunities for someone to get this right on the web.

In the course of working with some of Endeca’s more interesting clients, I started reading up on how the intelligence agencies address the challenges of making decisions, especially in the face of incomplete and contradictory evidence. I ran into a book called Psychology of Intelligence Analysis by former CIA analyst Richards Heuer. The entire book is available online, or you can hunt down a hard copy of the out-of-print book from your favorite used book seller.

Given the mixed record of the intelligence agencies over the past few decades, you might be wondering if the CIA is the best source for learning how to analyze intelligence. But this book is a gem. Even if the agencies don’t always practice what they preach (and the book makes a good case as to why), the book is an excellent tour through the literature on judgment and decision making.

If you’re already familiar with work by Herb Simon, Danny Kahneman, and Amos Tversky, then a lot of the ground he covers will be familiar–especially the third of the book that enumerates cognitive biases. I’m a big fan of the judgment and decision making literature myself. But I still found some great nuggets, particularly Chapter 8 on Analysis of Competing Hypotheses. Unlike most of the literature that focuses exclusively on demonstrating our systematic departures from rationality, Heuer hopes offer at least some constructive advice.

As someone who builds tools to help people make decisions using information that not only may be incomplete and contradictory, but also challenging to find in the first place, I’m very sensitive to how people’s cognitive biases affect their ability to use these tools effectively. One of the HCIR ’07 presentations by Jolie Martin and Michael Norton (who have worked with Max Bazerman) showed how the manner in which information was partitioned on retail web sites drove decisions, i.e., re-organizing the same information affected consumer’s decision process.

It may be tempting for us on the software side to wash our hands of our users’ cognitive biases. But such an approach would be short-sighted. As Heuer shows in his well-researched book, people not only have cognitive biases, but are unable to counter those biases simply by being made aware of them. Hence, if software tools are to help people make effective decisions, it is the job of us tool builders to build with those biases in mind, and to support processes like Analysis of Competing Hypotheses that try to compensate for human bias.

Many of the words that mark milestones in the history of technology, such as calculator and word processor, originally corresponded to people. Calculating had at least two lives as a technology breakthrough–first as a process, and then as a automatic means for executing that process. Thanks to inventions like calculators and computers, human beings have moved up the value chain to become scientists and engineers who take low-level details for granted.

Similarly, the advances in information science and retrieval have dramatically changed the role of a reference librarian.

Hopefully some of you old enough to remember card catalogs, They were certainly functional if you knew the exact title or author you were looking for, assuming the title wasn’t too generic or author too prolific. Where card catalogs fell short was in supporting exploratory search. In many cases, your best bet was to quite literally explore the stacks and hope that locality within the Dewey Decimal system sufficed for to support your information seeking needs. Alternatively, you could follow citation paths–the dead-tree precursor of surfing a hypertext collection.

For exploratory tasks, library patrons would turn to reference librarians, who would clarify the patrons’ needs through a process called the reference interview. According to Wikipedia:

A reference interview is composed of two segments:

1. An initial segment in which the librarian encourages the user to fully discuss the request.
2. A final segment in which the librarian asks questions to relate the request to the materials available in the library

A reference interview is structured (ideally) according to the following series of steps. First the library user states a question or describes a problem. The librarian then clarifies the user’s information need, sometimes leading him or her back from a request for a specific resource (which may not be the best one for the problem at hand) to the actual information need as it manifests in the library user’s life. Following that, the librarian suggests information resources that address the user’s information need, explaining the nature and scope of information they contain and soliciting feedback. The reference interview closes when the librarian has provided the appropriate information or a referral to an outside resource where it can be found, and the user confirms that he or she has received the information needed.

Fast forward to the present day. Thanks to modern search engines, title and author search are no longer tedious processes. Moreover, search engines are somewhat forgiving of users, offering spelling correction and inexact query matching. Libraries are still catching up with advances in technology, but the evolution is clearly under way.

However, search engines have not obviated the need for a reference interview. Excepting the simple cases of known item search, the typical information seeker needs help translating an information need into one or more search queries. And that information need may change as the seeker learns from the process.

But it should come as no surprise that information seeking support systems need to be more than search engines. The ideal information seeking support system emulates a reference librarian, stepping users through a structured process of clarification. Indeed, this is exactly what my colleagues and I at Endeca are trying to do in our work with libraries and more broadly in pursuing a vision of human computer information retrieval.

What then becomes of librarians? Much as calculators and computers did not obviate the need for mathematicians, I don’t see technology obviating the need for information scientists. Library schools have already evolved into information schools, and I have no doubt that their graduates will help establish the next generation of information seeking technology that makes today’s search engines seem as quaint as card catalogs.