This blew my mind, not only that an alphageek would prefer the (Google) interface on the left to the (Bing) interface on the right, but that the redditor alphageek community would so heavily upvote it.  The way I see it, this speaks directly to the issues of simplicity as storytelling vs. sparsity that I’ve talked about from time to time.  The interface on the left is anything but sparse.  In fact, it is extremely busy and filled with images,  a tool belt of various verticals (news, video images), query modification tools such as timelines and recency sorting, and query reformulation tools such as narrowly related searches (top middle) and broadly related searches (lower left).

In short, everything about it is “non-Googly”, i.e. non-sparse and non-clean.  Ironically, the Bing results for this particular query — which is held up as the example of what not to do — is the cleaner one.

So why is it that thousands of Google-loving redditors prefer the interface that is, well, more Bing-like?  Could it be that the user is finally starting to understand that simplicity is not the same thing as sparsity?  That what matters is the story?  The Google results in this case tell a really good story.  They give a concise overview of the latest matches and scores.  They link directly to highlights.  They give a concise overview of upcoming matches and the time at which each occurs.  And they acknowledge that when you search for “World Cup”, you’re not just trying to navigate to a single page.  Instead, you are “exploratorily” looking for as much information as you can about what is happening at the event as a whole, and perhaps even with football (soccer) as a whole. This is not just a “one box” answer. This is a whole “cluttered” set of rich information and interaction options.

That’s the story.  And if it takes a non-sparse (complex or cluttered) interface to tell that story, then so be it.  The story is more important than the strict adherence to sparsity.  Which is something that I’ve been hammering on about for at least the past half decade now.  It is just encouraging to see users finally start to acknowledge it.

Now, all we need to do is let the redditor community know that even though Google beat Bing on this one particular query, overall Bing has been pushing more of this story-appropriate, non-sparse, information rich (”cluttered”) interaction in their results.  What I wish users did more of is constantly rotate between the various engines, to know for themselves which queries work on which engines, and what each of the various engines are capable of.  Because the irony here is that the redditor that which “This is Why I Use Google, Not Bing” has chosen and interface that is much more Bing-like, and less traditionally “Googly”.

See also my related post, about two Googlers (Norvig and an anonymous employee) and their comments about Bing at the Semantic Technology conference in June 2009.

Update: In the couple of minutes between when I saw the reddit link and when I finished writing this post, the Google vs. Bing image went from 4th on the reddit home page (with ~500 upvotes) to 2nd (with ~750 upvotes).  Clearly this has touched a nerve.  It’s very interesting to see this reaction, especially because the preferred interface, again, is so traditionally non-Googly and cluttered.

Benjamin Scheibehenne, a psychologist at the University of Basel, was thinking along these lines when he decided (with Peter Todd and, later, Rainer Greifeneder) to design a range of experiments to figure out when choice demotivates, and when it does not.

But a curious thing happened almost immediately. They began by trying to replicate some classic experiments – such as the jam study, and a similar one with luxury chocolates. They couldn’t find any sign of the “choice is bad” effect. Neither the original Lepper-Iyengar experiments nor the new study appears to be at fault: the results are just different and we don’t know why.

After designing 10 different experiments in which participants were asked to make a choice, and finding very little evidence that variety caused any problems, Scheibehenne and his colleagues tried to assemble all the studies, published and unpublished, of the effect.

The average of all these studies suggests that offering lots of extra choices seems to make no important difference either way.

I’ll let that speak for itself, and will note only a few of my related blog posts from a year+ ago: Google Search Options and the Paradox of Choice and Ranked Lists and the Paradox of Choice.

as with all controversial issues, there’s a positive in google trying bing/image – that they’re not afraid to learn from competition

What Amit is referring to is the recent addition of gorgeous photographic images as search page background.  See for example this writeup: http://blogs.abcnews.com/theworldnewser/2010/06/google-vs-bing-copycat-picture-on-prominent-page.html

He is of course correct; Google is learning from the competition.  But there is another issue at play here, one that I don’t want to overlook because I feel it is very important.  It is the issue of simplicity.  What is simplicity?  How is it defined?  How is it measured? Conversely, what is complexity?  What is clutter?

For over a decade now, Google has essentially defined simplicity as sparsity.  Sparse backgrounds, lots of negative space, sparse color schemes, sparse auxiliary information (e.g. query term suggestions on the SERP page have only started appearing in the last year or two, despite the fact that such features existed 15 years ago in search engines of old such as Infoseek and Altavista).  The reason given was that people didn’t like clutter, that people like simplicity.  And in Google’s definition, simplicity equals sparsity.

I agree.  People do like simplicity.  I don’t question the veracity of that general sentiment.  What has always bothered me, though, is the equivocation of simplicity with sparsity.  I think a much better definition of simplicity is not the amount of information or colors or negative space on a page, but the story that a design, interface, interaction, or algorithm tells.  Something with a lot of colors and links and words can still be simple…if it tells a clear story!  Conversely, something with fewer colors and links (sparser) can be more complex, if the story that it communicates is muddy and not as purposely focused.

This brings us to the Bing background image.  In my opinion, the even though the inclusion of a background image is less sparse and more “cluttered” (more colors, more shapes, more textures), it actually assists in the telling of a clearer story.  Why?  Because it more cleanly separates foreground and background, subject and frame.  It provides compositional balance to the page.  The white query input box on white background (10+ years of Google design) is sparser, but the story that it tells is less clear because foreground and background are not as cleanly separated.  A white query input box on a richly colored and textured background tells a clearer, simpler story because the background image frames and separates the foreground query input box.  Furthermore, because you can now distinguish background and foreground, you can more clearly see that the query input box lies near the pleasing “rule of thirds” line, which aids further in the overall storytelling.

In short, I applaud this move by Google, just as I applaud it from Bing.  I never liked the white-on-white, because sparsity is not the same thing as simplicity.  Simplicity arises through good storytelling, not through minimalism.  No A/B testing will tell you this, though.  It’s a definitional issue that must be defined before you start your A/B tests.  Google has learned from the competition, as @akumar says.  But I hope that the lesson Google has learned is not just that users like pretty pictures.  I hope the lesson is that, when it comes to simplicity, there is a difference between sparsity and storytelling.

See also my posts: The Tyranny of Simplicity, The Tyranny of Simplicity, Redux, and The Craft of Storytelling.  I also found this older discussion on Google’s Lively to be a fascinating read.  In my understanding, the issue of “necessary complexity” that the author of that post hammers home about is related to the issue of storytelling.  Too much sparsity (of interaction in Lively’s case) leads to an inability to tell a clear story.  Simplicity is storytelling, not sparsity.

We’ve long believed that personalization makes search more relevant and fun. For nearly five years, we’ve been tailoring results with personalized search. Today we’re announcing a new feature in search that makes it easier for you to mark and rediscover your favorite web content — stars.  With stars, you can simply click the star marker on any search result or map and the next time you perform a search, that item will appear in a special list right at the top of your results when relevant. That means if you star the official websites for your favorite football teams, you might see those results right at the top of your next search for [nfl].

So it sounds to me like this is a sort of bookmarking.  What it not as obviously, however, is what this sentence means:“the next time you perform a search, that item will appear in a special list right at the top of your results when relevant”.  Does that mean the next time you perform the same search (e.g. [nfl]) that starred item will appear at the top?  Or is it more dynamic than that?  I.e., if I happen to perform the search [new england patriots], and that same link that I’d previously starred after executing the [nfl] query happens to be ranked in the top k, will it again appear at the top of my list?  (And if so, what is the cutoff/threshold for k?)  Similarly, if Google’s ranking of my original [nfl] query changes, due to shifting PageRank calculations, changes in freshness, or any of the hundreds++ of other signals that go into the ranking algorithm, and my particular starred web page no longer appears in the top k because it is no longer relevant to the [nfl] query using the signal vector from the current state of index, will the starred item not appear?  After all, Google says that the starred item will only appear if it is relevant, and if it is no longer relevant to the [nfl] query, as determined by Google’s relevance algorithm, then it won’t appear?  Even though I had previously starred it with respect to that exact query?

The post continues:

In our testing, we learned that people really liked the idea of marking a website for future reference, but they didn’t like changing the order of Google’s organic search results. With stars, we’ve created a lightweight and flexible way for people to mark and rediscover web content.

Now I am thoroughly confused.  People didn’t like changing the order of Google’s organic search results, but at the same time, they claim earlier in the post that “For nearly five years, we’ve been tailoring results with personalized search.”  What does it mean to personalize search results, if not to change the order of Google’s organic search results? (Quoting the earlier post:

With the launch of Personalized Search, you can use that search history you’ve been building to get better results. You probably won’t notice much difference at first, but as your search history grows, your personalized results will gradually improve.

So if users didn’t like changing the order of the organic search results, does this mean that Google has turned off (or will be turning off) personalization completely for all signed-in users?  Or does personalization co-exist with explicit starring/bookmarks?  If so, how exactly does that work?  Will Google change the order (personalize) your organic results using only the signals of query history and implicit relevance (i.e. clickthrough), but not the signal of explicit starring?  That’s even more confusing…the amount of mental jazz involved is a bit overwhelming.  Sure, the interface jazz is kept to a minimum, but at the expense of making the user’s mental model of what the search engine is actually doing for him or her even more muddled.

Perhaps the best way to sort out this confusion is to dive in headfirst and start playing around with the system, seeing what it actually does and when.  But I personally have a difficult time generating the gumption to use a feature for which I have an unclear mental model, an unclear understanding of what it is trying to do for me, how it might change, when it might or might not magically appear.  Especially when some of my actions affect the state of the system and others do not.

One thing I do like about this feature, however, is that it uses out-of-band displays to show different types of information.  Rather than trying to mix global/non-personalized results, implicit personalized results, and starred results, it lets you know via a separate channel whether there is any information that you have previously starred.  This is an IR design principle that I would like to see more of — separate goals in separate channels.  Examples of different IR goals include navigation, re-finding, discovery, exploration, etc.  Rather than trying to mix results from all of these goals into a single channel (a single ranked list) it is quite useful to separate each goal from the other.  This new Google interface does that.  What exactly the goal attached to that separate channel is, again, unclear.  But the existence of a separate channel is an interesting and exciting approach, one that I hope to see more of.

Let me be clear about Aardvark: What that service does is help you comb through a network of people to find those individuals who have the highest likelihood of holding the answer to your information need.  Somebody has the answer; you just don’t know who it is.  So Aardvark helps you find that somebody.  The reason this is different from what I am talking about with explicit collaboration is that in this latter case, you already know who it is that you want to work with on resolving a shared information need.  You want to work with a relationship partner on finding an apartment.  You want to work with a business colleague on finding potential markets for a new product.  You want to work with some buddies on planning a road trip.  In all of these situations, your partner, your colleague, and your buddies don’t already have the answers that you seek.  But you do know that you want to work with them to find those answers because they have the same need that you do.  Your partner wants to live with you, your business colleague wants to work with you, and your buddies want to travel with you.  This is what explicitly collaborative information seeking is about, and it’s not the same thing as the “collaborative” category discussed in the panel.

Case in point: Take a look at the panel’s slides: http://www.slideshare.net/bmevans/introductory-slides.  Slide 9 outlines the two main social strategies: (1) Ask the network, and (2) embark alone.  This misses a third major, but as yet untapped, strategy: (3) embark together.

A good way to think about this is in terms of information seeking.  In both the (1) ask the network and (2) embark alone strategies, there is only a single user with an actual information need, a single person who is actively seeking information.  Using Aardvark, he or she is asking other people in the network if they are able to give an answer to satisfy that need.  But those other individuals do not actively share your information need.  They already either (1) have the information that you seek, and thus already have a satisfied information need, or (2) do not have the information you seek, but do not care, i.e. they do not share your information need (they aren’t going to move in with you, or go on that road trip with you).  When you ask the network, you are not actually involved in collaborative information seeking.  There is only a single seeker: You.  You are simply tapping into the network to find those people who already have the information you need.  It is still the single individual, not the network, that has the information need and that is actively engaged in the seeking process.

But embarking together with one or two other individuals who also lack information, i.e. engaging in explicitly collaborative information seeking, is a entirely different process.  In this case, there are at least two information seekers, two people who have a shared, as-yet-unsatisfied, information need.  Now, there are a number of different ways you can build systems and design interfaces to support these multiple seekers in their task.  I’ve written a lot about such systems on this blog and on the FXPAL blog, and will not go into it in further detail right now.  The point is simply that embarking together is an information seeking strategy that was not covered by any of the existing methods.  It is not the same as asking the network.  It is not the same as embarking alone.  It is a third process, a third strategy, and one that remains quite untapped in today’s marketplace.

Update: I have a final quick example.  On his live blog, Danny Sullivan paraphrases Max from Aardvark: “We want to do that across communication channels, so you can find partners to go bike with”.  That’s Aardvarkian social search: You want to find the people to go biking with.  Collaborative search is the next phase.  Once you’ve found the people that you explicitly know that you want to go biking with, how do you find out where you want to go?  You know about all the bike trails around your house.  Your new biking partner knows about all the trails near her house.  But neither of you know about the trails that exist halfway between both of your houses.  Ideally, you’d like to find one of those trails that is good for both of you, because neither of you is aware of them.  (And why should you have been? Before meeting your partner, you had no reason to venture away from your favorite nearby trails.) THAT is explicitly collaborative information seeking.  When both of you actively look for new bike trails, that is embarking together.

Collective (gathering advice from a crowd)

Friend Filtered (using your friends)

Collaborative (asking a friend — see also our The Rise Of Help Engines: Twitter & Aardvark article)

The version that was left out was the type of search in which you don’t just ask a friend for an answer (e.g. Twitter and Aardvark), but the type of search in which you actively engage with a specific person to work on on a jointly-shared information need.  For example, imagine a couple looking to rent an apartment.  It’s not like one person in the couple can ask the other one “where should we live?”  The point is that both people do not know.  And so you can imagine an information retrieval system that has, built in, the capability to be multi-searcher aware.  Both people can work on the same task at the same time.

This is not what Aardvark does.  This is not what Twitter does. This is also not friend-filtered; this is also not collective.  It is a fourth type, a distinction that seems to have been missed by the panel — search in which a small team of people actively work together, and the search system actively mediates between them, helping the group as a whole find information that no individual already knows, and that no individual would have easily found, had that person been working alone.   For more information on this oft-ignored area, please see our earlier series of posts.

Next week, Gene Golovchinsky and I are moderating a pair of panels at the SSM workshop.  So we spent some time this week asking ourselves these definitional questions in preparation for the panel.  We came up with a lightweight taxonomy, and have done a few classifications/examples of existing systems into that taxonomy.  Whether or not you are one of the 80 participants in the workshop, I would invite you to take a look at our framework and comment or critique where necessary.  Here’s the link to Gene’s writeup:

We think the phrase ’search in social media’ has been used to refer to both the information being searched, and to the process for doing so. The information is standard user-generated content — tweets, blog posts, comment threads, tags, etc. The process, however, seems less well understood…It will be interesting to see how these ideas will be transformed by the discussion at the workshop. In any case, having a language with which to talk about phenomena is a prerequisite to articulating a research agenda, particularly in a young and multi-disciplinary field.

Please note, however, that one topic that will probably not be covered is the difference between social search (process) and collaborative search (process).  The latter workshop will be held a few days later at CSCW.  For an interesting thread on the distinction between the two, please see another FXPAL post from March of last year.

This experiment goes unmentioned by Russkin-Gutman, a major omission since it relates so closely to his subject. Even more notable was how the advanced chess experiment continued. In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)

Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

This result seems awfully similar to some of the other results I’ve reported on in the past.  For example, see this paper by Amatriain:

Data is always important, but what struck me in the writeup was his discovery that the biggest advances came not from accumulation of massive amount of data, log files, clicks, etc.  Rather, while dozens and dozens of researchers around the world were struggling to reach that coveted 10% improvement by eking out every last drop of value from large data-only methods, Amatriain comparatively easily blew past that ceiling and hit 14%.  How?  He simply asked users to denoise their existing data by rerating a few items.  In short, Amatriain resorted to HCIR:

See also Tessa Lau’s post about how good interaction design trumps smart algorithms:

I come to the field of HCI via a background in AI, having learned the hard way that good interaction design trumps smart algorithms in the quest to deploy software that has an impact on millions of users. Currently a researcher at IBM’s Almaden Research Center, I lead a team that is exploring new ways of capturing and sharing knowledge about how people interact with the web.  We conduct HCI research in designing and developing new interaction paradigms for end-user programming.

See also two of my previous posts, More and Faster versus Smarter and More Effective and A Bird in the Hand.

The theme that I see is that, while big data approaches do work well, what works even better is a small amount of user interaction.  With big data methods  (even ones that incorporate human interaction in the form of massive log data) all you can do is make inferences about what is good and what is not good.  The more historical user data you have, the more correct your inference about the current scenario is likely to be.  But none of it is as correct as receiving explicit feedback from the user, and turning a probability into a certainty.

And that’s where I see good interaction design coming into play.  By turning a probability into a certainty, your back end algorithms can stop wasting their CPU cycles doing all the inferential heavy lifting about what the user is actually trying to say or do, and can start using their CPU cycles to explore a wider range of consequences of that informational certainty.

HOW IS THE INTERNET CHANGING THE WAY YOU THINK?

As an Information Retrieval research scientist, I of course was quite interested in what search folks had to say.  I found this blurb from Marissa Mayer intriguing:

It’s not what you know, it’s what you can find out. The Internet has put at the forefront resourcefulness and critical-thinking and relegated memorization of rote facts to mental exercise or enjoyment. Because of the abundance of information and this new emphasis on resourcefulness, the Internet creates a sense that anything is knowable or findable — as long as you can construct the right search, find the right tool, or connect to the right people. The Internet empowers better decision-making and a more efficient use of time…

The Web has also enabled amazing dynamic visualizations, where an ideal presentation of information is constructed — a table of comparisons or a data-enhanced map, for example. These visualizations — be it news from around the world displayed on a globe or a sortable table of airfares — can greatly enhance our understanding of the world or our sense of opportunity. We can understand in an instant what would have taken months to create just a few short years ago. Yet, the Internet’s lack of structure means that it is not possible to construct these types of visualizations over any or all data. To achieve true automated, general understanding and visualization, we will need much better machine learning, entity extraction, and semantics capable of operating at vast scale.

It sounds like there is an increased awareness of (and respect for) Exploratory Search.  I’ve heard this via private channels, but this is the first time I’ve seen an acknowledgment of the need for more exploratory search from such an official channel.

I do want to point out, however, that in order to make this work at web scale, we won’t just need better automated methods.  I.e. we cannot rely solely on machine learning, entity extraction, or web-scale semantics.  Rather, what is also desperately needed is a way for the user him- or herself to inject personal semantics and structure into the search, visualization, and comparison process.  The search engine itself needs to be responsive to the structure that the user is giving to it, and rearrange itself around that information.

I am afraid that I am not being very clear in the vision that I’m attempting to lay out, so let me draw an analogy to parametric and non-parametric statistical modeling.  In parametric modeling, you assume that your data is distributed according to some function (say, Gaussian) and then you try and find those parameters that best fit the data.  On the other hand, with non-parametric modeling you make no such assumption.  You simply let the data describe itself through its own correlations and patterns.

By analogy: Assuming that the only way to visualize and compare information (do exploratory search) on the web is to rely on machine learning to do entity extraction and web-scale semantics is like assuming that one has to have a parametric model.  It helps, but it is not absolutely necessary.  My vision is for another approach, one analogous to non-parametric methods: Let the user give feedback on the relationship between items that he or she has examined during the search process and then use that comparison information to build personalized visualization or comparison tool for that user’s specific information need, from the ground up.  Don’t rely on the parametric form of semantic categories or named entities.  Use bottom-up patterns to facilitate organization and comparison, discovery and learning, decision making and exploration.  More importantly, use the feedback provided by the user (e.g. “these two items are similar”, and “these two items are not”) to drive your online, bottom-up exploration.

We have to get away from this attempt to solve the exploration problem ahead of time, off-line, before the user has ever issued a query.  That’s the parametric way of thinking, the way that presumes that categories and labels and entities are the best way of tackling organization and discovery.  Rather, we have to become better at involving the user, the person doing the exploration, in the feedback loop, and not rely solely on pre-computed, machine-learning-extracted entities.

Unlike navigational search, in which users are rarely willing to do any extra work themselves, users engaged in exploratory search by their very nature desire to interact more with the system and put more of their own sweat and tears into the search process.  They would not be exploring, if they weren’t.  So why not make use of this user willingness?

Computational resources are going to be a challenge.  But that’s where Google’s new commitment to openness (and Yahoo!’s initial, existing commitment) comes in handy.  There should be a willingness to offload some of the computation (and therefore also the search data itself) to the user’s own computer.  Instead of SETI@Home, we could have SEARCH@Home.  Let the user’s underutilized processing power be partially responsible for computing some of these bottom-up patterns in his or her own search data that will help make dynamic visualization a reality.  Make the user’s own computer partially responsible for the additional necessary processing.

Mayer is correct: “The Internet has put at the forefront resourcefulness and critical-thinking and relegated memorization of rote facts to mental exercise or enjoyment. Because of the abundance of information and this new emphasis on resourcefulness, the Internet creates a sense that anything is knowable or findable — as long as you can construct the right search, find the right tool, or connect to the right people.“  We should be developing systems that enable the users to construct the right search. The user should be able to rely on our her resourcefulness to mash up and explore the data herself, to shed light on patterns of information hitherto unknowable by single-line input box navigational search.  Users should be able to apply critical thinking to their search process in a way that makes sense to the user, not in a way that has been pre-computed through some semantic category and machine learning classifier.  And a good search engine should be a valuable partner in this process, by way of flexibility and openness, not by way of constraint and closedness.

Only then will we, the users of these systems, be able to find out what we previously could not find out.  At least, that is how the Internet is changing the way I think.

The article focuses on YouTube’s “plans to rely more heavily on personalization and ties between users to refine recommendations” and “suggesting videos that users may want to watch based on what they have watched before, or on what others with similar tastes have enjoyed.”  What is striking about this is how little this has to do with search. As described in the article, what YouTube needs to do is entertain people who are bored but do not entirely know what they want. YouTube wants to get from users spending “15 minutes a day on the site” closer to the “five hours in front of the television.” This is entertainment, not search. Passive discovery, playlists of content, deep classification hierarchies, well maintained catalogs, and recommendations of what to watch next will play a part; keyword search likely will play a lesser role.

My feeling is that the dichotomy that is being drawn does not exhaustively cover the space.  I would characterize the space using the following two orthogonal dimensions: (1) Information Need Clarity and (2) User Engagement.  The first dimension (clarity) is related to the degree with which the user understands his or her own information need, i.e. has something specific in mind that he is looking for and/or understands what he needs to do to find it.  That need may either be well understood, or (to borrow Nick Belkin’s terminology) “anomalous”: The user doesn’t know what he or she doesn’t know.  The second dimension is related to the level at which the user applies himself to the information seeking process.  That level may be active or passive.

Greg points out two modes: “Active Understood” (typical navigational web search) and “Passive Anomalous” (entertainment/discovery/recommendation).  But I believe that there are more than these two modes.  A large, interesting design space opens up when one realizes that information seeking can be “Active Anomalous” and “Passive Understood“.

Exploratory Search is a good example of Active Anomalous seeking.  One doesn’t yet fully know or understand what it is that one is looking for, but at the same time one is willing to engage with an information system in order to discover what it is that he or she does not yet know.  And the system itself is designed not necessarily toward trying to answer a well understood need, but toward helping the user map out and better comprehend a space.

Collaborative Information Seeking (see here and here and here) is a good example of where an need may be well understood, but a user does not necessarily have to actively express every last query detail in order to get more information on a topic.  Why not?  Because when User #1 is explicitly collaborating with User #2, an algorithmic mediation engine can push some of User #2’s activity on to User #1 without requiring User #1 to make additional effort.  Note that I am not implying that every aspect of collaborative information seeking is passive; quite the contrary, as it requires at least one co-collaborator to be active.  I am only pointing out that it is a domain in which it becomes possible for a user to passively obtain specific information on a well understood need.

There is a lot discussion in the Information Retrieval Community on the similarities and differences between Search and Recommendation.  A fruitful tension opens up as one travels back and forth along the diagonal from Active Understood to Passive Anomalous; the two approaches often end up complementing each other.  Where I see much less discussion is on the tension that opens up along the other diagonal, between Passive Understood and Active Anomalous.  When Exploratory Search meets Collaborative Information Seeking, it yields Collaborative Exploratory Search and a whole host of interesting possibilities.  Over the coming year I will be blogging more about the tension along this alternative diagonal (both here at on the FXPAL blog) and what it means for the Information Retrieval systems I and others are designing.  Happy 2010!