micro-IR
I’ve been watching with interest as Apple’s iphone/ipod_touch app store has grown and matured over the last couple of year (yes, I know, me and almost everyone else). Interacting with apps on my own, and more recently, building a few, has started me thinking about what I perceive to be an interesting, and I think, novel mode of information interaction.
For lack of a better term, I think of this phenomenon as “micro information retrieval” (micro-IR).
By micro-IR I mean the practice of farming information needs out across multiple applications. Each of these micro-IR applications is built around a tightly constrained problem space, and I think it’s this constraint that makes micro-IR interesting.
A couple of examples (apologies for any appearance of commercial endorsement; none intended):
- the yelp app: find, say, restaurants near me
- loopt: find friends near me
- Barnes and Noble app: find info on the book in this photo I took
- shazam: find the song that is playing into the iphone mic.
These examples swing close to simple database lookups. But if we take a longer view, a more interesting dynamic comes up. The apps are simple because each one solves a problem that is tightly constrained, answering a question that would involve complicated interaction in its absence.
By way of a few more examples, I am currently developing an app that answers the question: how many gallons of oil would it take to prepare a given recipe? The app then ranks candidate recipes in increasing order of petroleum consumption.
And it’s not the case that these sorts of interactions are limited to mobile devices. Thanks to Gene Golovchinsky for pointing me towards Blueprint an Eclipse plugin that allows users to search for code snippets from within their IDE, leveraging Flex syntax to finesse the search.
Trying to lasso these examples together in efforts to triangulate on what micro-IR actually is, I’ll note a few overarching commonalities that I see here:
- In ad hoc (text) IR a principal intellectual challenge lies in modeling ‘aboutness.’ In micro-IR settings, the creativity comes into play in posing a useful (and tractable) question to answer. The engineering comes easily after that.
- The constrained nature of micro-IR applications leads to a lightweight articulation of information need. There is a tight coupling here between task, query, and the unit of retrieval, a dynamic that I think is compelling. Pushing this a bit farther, we might consider the simple act of choosing to use a particular application from those apps on a user’s palette as part of the information need expression.
- The tight coupling of task to data to ‘query’ enables a strong contextual element to inform the interaction. Context constitutes the foreground of the micro-IR interaction.
I don’t want to overstate the distinction between micro- and macro-IR. Of course applications fall along a spectrum of their similarity to the modalities I’ve laid out here. But I do think that being aware of micro-IR system characteristics is worthwhile. Aside from an inherent innovation to how people interact with information, micro-IR opens the door to small-scale developers gaining a wide audience (i.e. the barrier to entry is low). And concomitant with this is the new monetization model at work in the app store.
I hope readers will comment on this: is micro-IR something at all? Is it actually related to IR? How might we turn our eye to micro-IR with respect to generating bona fide research? Surely there are better example systems than those I’ve listed…
Any particular information retrieval problem is a function of queries and documents. I try to approach this in as general a way as possible. For me, the only difference between micro and macro IR is text. In fact, I would say, in many ways, pure text IR is _more_ constrained than other IR tasks you mention.
Hi, Miles. (we met in ECIR)
I agree with Fernando in that we’ve been dealing with very constrained features (i.e. textual similarity) in traditional IR.
What characterize Micro IR seems to be that the context (searcher goal) is known, with domain-specific notion of relevance (goodness) and similarity measures. I guess current IR frameworks (including learning to rank) can accmmodiate most of these problems. After all, it’s still about combining evidences, albeit of a different type.
This also reminds me of vertical search, where we need to infer user’s information goal given only query-words. Here, each information type may have somewhat different notion of relevance as well, although not to the extent you talk about in Micro IR.
Great post! I just posted my own reaction here:
http://thenoisychannel.com/2009/09/12/micro-vs-macro-information-retrieval/
I think one of the key issues here is whether the retrieved information is actionable, that is, whether enough context is represented in the system to suggest meaningful actions based on retrieved results. Here’s my take.