Month: November 2009

Web search is a fiercely competitive space–as Google points out, “competition is just one click away“. In practice, I take that claim with a grain of salt–but I do think the switching costs are much lower than in most competitive markets. With that in mind, it’s interesting to look at what happens if you search for the name of one of the major search engines on one of its competitor’s sites.

Google returns standard results for such searches:

Bing is generous to a fault, saving you a click if you choose to use one of its leading competitors:

Finally Yahoo, whose CEO claims “we have never been a search company,” seems quite eager to keep searchers from going elsewhere:

It’s easy to dismiss these queries as corner cases, but the logs show that they really happen. And, as browsers increasingly blur the line between an address bar and a search box, it’s not unreasonable to consider that switches between search engines are likely to commence with such queries.

Earlier this week, Marti Hearst gave a Tech Talk at Google about her recently published book, Search User Interfaces. Fortunately for those of us who missed (myself included!), it is now available on YouTube. Enjoy! (via Jon Elsas)

One of the interesting challenges we face as both both developers and consumers of search technology is that social signals are a double-edged sword. On one hand, social signals have proven essential in distinguishing signal from noise–be they links, re-tweets, or any number other ways that online consumers (or more correctly “prosumers”) actively and passively communicate value judgments about information. On the other hand, our reliance on these social signals makes us vulnerable to positive feedback and spammers.

Consider MusicLab, an “experimental study of self-fulfilling prophecies in an artificial cultural market“. In this study, sociologists Matt Salganik, Peter Dodds, and Duncan Watts manipulated the social information available to consumers (specifically teens) regarding their peers’ musical tastes. The experimenters’ goal was to empirically validate a quantitative model of social contagion.

But we can look at this study another way: by isolating the social factors that influence musical taste, the experimenters were also isolating the non-social signal–in theory, how popular a song would be in the absence of social signaling. Indeed, they found that, if they measured a song’s quality by isolating out the social factor, “the best songs never do very badly, and the worst songs never do extremely well, but almost any other result is possible”.

It’s interesting–interesting to me, at least!–to ask if search engines can do the same for search. One of the frequent objections to link-based authority measures like PageRank is that they make the rich get richer. “Real-time” variants like re-tweet frequency (and even TunkRank) suffer from the same weakness. Unchecked, these measures can cause authority / influence market has to resemble a winner-take-all market.

It strikes me as interesting to learn from cases where searchers swim upstream against the social signals to find information. Of course, you may already see the contradiction–this is just another kind of social signaling! Still, it seems like it might be a way to hedge our bets and against the weaknesses of positive feedback and spammers. In a similar vein, we might look at how users find information that suffers from poor accessibility or retrievability.

I don’t have answers about how to pursue such an approach, or whether it would even be feasible to do so. But I hope you agree with me that it’s an interesting question.

Google’s recently released Image Swirl is slick. But I’ve been struggling to figure out whether it’s useful or simply a showcase for cool technology.

And that’s prompted me to think about the overloaded term “exploratory search“. A while back, I tried to define exploratory search based on what it is not. This time, let me aim to positively characterize what I see as its two primary use cases:

1. I know what I want, but I don’t know how to describe it.
2. I don’t know what I want, but I hope to figure it out once I see what’s out there.

The first use case cries out for tools that support query refinement or elaboration. Existing tools span a range from suggesting spelling corrections (aka “did you mean”) to offering semantically or statistically related searches that hopefully provide the user with at least a step in the right direction. One of my favorite approaches, faceted search, is primarily used to support query refinement through progressive narrowing of an initial search query.

The second “I don’t know what I want” use case is fuzzier. In the language of machine learning, this use case is unsupervised, while the previous one is supervised. In general, it’s a lot harder to define or evaluate outcomes for unsupervised scenarios. Indeed, Hal Daume has argued that we should only do unsupervised learning if we do not have a trustworthy automatic evaluation metric. That’s a strong position, and you can see some of the counterarguments in his comment thread. But, going back to our scenario, it’s really hard to judge the effectiveness of tools like similarity browsing when they support exploration in the absence of any concrete goal.

With that in mind, I’ll reserve judgment on the utility of tools like Image Swirl. To the extent that it aims at the first use case, clustering images for a particular search, I’m ambivalent. I’d prefer a more transparent interface, in which I have more of a sense of control over the navigational experience. I suspect it is more aimed at the second use case, offering a compact visualization of what is out there.

Besides, as some folks have brought up at the HCIR workshops, it’s important that we make information seeking fun. And Swirl certainly scores on that front.

A computer-implemented method for operating a device, the method comprising:
disabling a function of an operating system in a device;
presenting an advertisement in the device while the function is disabled;
and enabling the function in response to the advertisement ending.

So reads the first claim from a patent application that Apple recently filed (with Steve Jobs as first inventor, no less!) for technology to deliver a rather compelling ad-supported business model. Or perhaps the better word is compulsory. You can read an analysis by Randall Stross in the New York Times.

I agree with Stross that it’s hard to imagine Apple ever implementing the technology described by the patent application–indeed, Apple has been one of the few success stories for paid digital content models. That said, the approach does feel like at least one endpoint for the ad-supported model–it guarantees the advertisers the attention that they are paying for by subsidizing content or services.

The advertising business is a bit more top of mind for me, now that it pays my salary. Google’s approach, however, follows the aphorism that honey catches more flies than vinegar: it tries to target ads well enough that users want to click on them, rather than to simply endure them as a cost of subsidizing free services. Google’s revenue (and the popularity of PPC models in general) is a testament to the success of this approach, my occasional rant notwithstanding.

In general, the industry seems to have found a compromise in how aggressively to push ads at users. Users can safely ignore (or even block) sponsored links, but few people do.  Pre-roll ads on video sites (i.e., advertising before a video starts)  are more invasive, but a number of sites let users skip them. You can read why the YouTube folks are testing this approach. Advertisers–or at least ad-supported services–seem to recognize that they can’t cross the line between pursuing users’ attention and annoying users to the point of alienation.

Still, technology like Apple’s patent application describes shows that it is possible for the ad-supported model to take a more more aggressive approach. Part of me wonders if more aggressive ad-supported models would revitalize paid content models, as users would stop perceiving the former as free. But I suspect that the gentler ad-supported model is here to stay, and that it will continue to strive toward the point of optimal effectiveness.

I’m barely 24 hours into my new life as a Googler, and I’ve already gotten lots of questions! Here at the answers to a few of them:

Will I continue blogging at The Noisy Channel?

Absolutely! I’m committed to posting at least weekly, and I’ll try to do better than that once I’m settled into my new environment.

Will I participate in scholarly conferences and workshops?

Of course! I’m co-organizing SSM 2010, which will be held in conjunction with WSDM 2010 in February, and of course HCIR 2010, which will be held in conjunction with IIiX 2010 in August. You probably won’t see me at vendor fests, but I do hope to continue bringing industry practitioners and academic researchers together.

Will I blog about Google?

I certainly won’t disclose any confidential information–people get fired for that–or worse. And, given how much access I will have to such information, I will err on the side of caution, only discussing information that I’m sure Google has released to the general public. Beyond that, I’ll exercise common sense. I don’t want to either come across as a shill for my employer or to spar with my new colleagues in public. Subject to those constraints, however, I can and will blog about Google.

Can I get you a job at Google?

I can advise you and connect you to a recruiter, but that’s the limit of my power. The hiring process here is specifically designed to prevent any individual from manipulating it–even me!

Will I talk about what I’m working on?

See above regarding confidential information. I’ll be delighted to talk about anything I’m working on that Google has decided to disclose publicly.

Does Google know about my karaoke habit?

Too late, they’ve already signed the offer letter. 🙂