Here's the promise
"Google Now gets you just the right information at just the right time.
I've blogged last year about what Siri could possibly do with library related functions, while interesting most of those functions can be pretty much be done now with sufficient coding skills and apis, but Google Now sets a much higher bar, and even now it is pretty limited in what it can do.
Still, one can dream. What if 10 years down the road, Google Now type predictive technology has matured and is the norm and perhaps married with Learning analytics , how would libraries use it to serve users?
One thing to note is that serving our users or members the information they need, when they need it, is the essence of librarianship.
I've blogged in the past that user needs and demands are essentially predictable in advance , as in for an academic library predictably every year in Aug new students would get lost in the library, near Oct when the exams are going to start in the next month, they start wondering where to get past year exam papers and end Nov/ early Dec, they start wondering about library hours during vacation and the possibility of bringing books home on long term loans with them etc.
So one could anticipate such events in advance an post (or even preschedule) information on such common needs to be emailed, tweeted or posted on Facebook in advance.
The problem with this approach of course is that you can also target generalities, most undergraduates would need this in Oct and need that in Nov but not all.
Susan Gibson & Nancy Fried Foster's work at Rochester University, showed that among other things, teaching fresh undegraduates information literacy during orientation week is not the best use of time, as the students are grappling with other more immediate needs such as settling in dorms and the prospect of writing term papers seems to be far off.
Of course that is what we have subject liaisons for, to specifically target different segments, but even subject liaisons can't go to everyone in person & their timing may be off unless they are very experienced.
Here's what I envision, an app - call it "Library Now" would be a courseware app similar to Blackboard Mobile Learn or my institution's own IVLE app. As such it would know which courses you are enrolled in and deadlines for assignments would be linked to your calender.
Based on such deadlines it would intelligently display needed resources. For example it would warn students perhaps a few weeks before the first assignment is due the availability of a writing hub. If married with learning analytic it would know which students would need more help with writing and even prompt the student to contact his specific subject liaison.
It could alert students to either upcoming sessions by libraries of interest, or push learning objects such as short videos specially crafted for each specific need eg videos on citing references or more specific subject related resources.
The location based aspect would detect the user is nearby the library or bookdrops and would display books that are due soon and might prompt him to return.
Within or near the library it might tell you that your subject liaison is currently in or on desk and you might want to pop-in to ask questions.
Depending on how it handles indoors location , it might tell you books or resources of interest as you walk past (think book recommender like Huddersfield's but more advanced ).
Like Google it would track your searches including library related ones like with the web scale discovery system Summon and combined with data of your modules give you more relevant results and perhaps even learn to anticipate and serve up relevant library guides, databases and Faqs.
To some extent this is already done, for example in MLibrary's Putting a Librarian's Face on Search.
"When you do a search on the University of Michigan Library's web site, you get not only results from the catalog, web site, online journal and database collections, and more, you also get a librarian who is a subject specialist related to your search term. While the matching is not perfect, it provides a human face on search results. So, for example, if you search for "Kant," in addition to books and databases, you also get the subject specialist librarians for humanities and philosophy."
More recently, I stumbled upon some work that tries to map searches in Ebsco Discovery Service to call number ranges and then the appropriate research guides to display.
And the recently launched Summon launched a suit of services called Summon Suggestions, which allows librarians to enhance search results by creating smart-tags to trigger databases or even "best bets" (Some text + link) that appears on the top of search results.
A advanced system that learns using these and other methods would made up a profile of what you are generally interested in and hold it in reserve and when you most need it, it could recommend these resources. It could even when asked to "explain" why such recommendations were made.
It might even learn your studying habits and give you recommendations on where in the library or campus you might like studying at, depending on noise level , availability etc.
The possibilities are endless and I only touched on supporting students and not researchers.
Okay I've gone overboard with the possibilities, such a system would be at HAL level and might go Skynet :)
One little problem besides that, even if all this was technically possible say in 2020, many libraries would still hold back because of the fear of violating patron privacy.
A recent article - As Libraries Go Digital, Sharing of Data Is at Odds With Tradition of Privacy spells out some of the concerns from libraries tweeting recently returned books to recommendation systems.
Again this is the age-old question our profession faces how much privacy should we safe-guard for our users, when many of them don't really care and pretty much give it whole-sale on far less trust worthy entities.
Should we handicap our abilities to compete to the point that even anonymized data collected on aggregate level with safeguards that are used for recommender systems come into question? Much like personalized, individualised data that a system like Google Now uses?