Search in Social Media

What is Social Search as opposed to Social Media?  Social Search in Media?  Search in Social Media?

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 [...]

January 29th, 2010 | Category: General, Information Retrieval Foundations | One comment

Kasparov and Good Interaction Design

A NYT books article about Kasparov and chess, and the relationship between humans, machines, and decision processes is making the Twitter rounds today.  I don’t have time at the moment to write a long comment about it, but I do want to point out that it supports a position that I’ve been taking on this blog for some time now:

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.  Continue reading…

January 25th, 2010 | Category: Explanatory Search, Exploratory Search, Information Retrieval Foundations | 2 comments

What You Can Find Out

The Edge has published their annual question for 2010:

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.  Continue reading…

January 12th, 2010 | Category: Exploratory Search, Information Retrieval Foundations | 6 comments

Search versus Recommendation: Not The Only Tension

Greg Linden has an interesting post on Search on a domain like YouTube.  I reproduce it here because I would like to elaborate on it:

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 [...]

January 5th, 2010 | Category: Collaborative Information Seeking, Exploratory Search, Information Retrieval Foundations | 4 comments