What do you want a user to do as a result of your recommendation?
If you are offering recommendations to users then you may have some specific outcomes that you want to achieve. On Amazon the recommendations ‘people who bought this also bought that’ would firmly seem to be aiming to increase sales. I’d wonder with Amazon whether it also broadens or narrows the range of titles that are sold. Does it encourage customers to buy items they wouldn’t normally have considered? I’m sure that is true, but is it reinforcing ‘bestseller’ lists by encouraging customers to buy the same items other people have bought or is it encouraging them to buy items from backlists. Is it exploiting the ‘long-tail’ of books that are available?
There’s evidence from Huddersfield that adding recommendations to the catalogue increased the numbers of titles being borrowed. Reported here in Dave Pattern’s blog. His slides have an interesting chart (reproduced on the left) showing how the range of titles borrowed increased. So that is clearly impacting on the long-tail of stock within a library. The SALT Project with John Rylands and MIMAS is specifically looking at how recommendations might encourage humanities researchers to exploit underused materials in catalogues. SALT, like RISE, is being funded as part of the JISC Activity Data strand.
With the RISE project we are working with a narrow set of data in that the recommendations database will only contain entries for articles that have been accessed already. So there is less direct opportunity to exploit the long-tail of articles by showing them as recommendations. But our interface will be using the discovery service search so users see EDS results directly from that service alongside our recommendations, so there will be some potential broadening of the recommendations in the database.
One other aspect about recommendations that has come up is the extent to which they may be time-dependent for HE libraries. Talking through some stuff about RISE with Tony Hirst (his blog is at ouseful.info) the other week and he challenged us to think about when recommendations will be useful to a student.
We build and run our courses in a linear fashion, so students go step by step through their studies doing assignment x on subject y and looking at resources z. Then they move on to the next piece of coursework. So with recommendations reflecting what happened in the past there’s a danger that the articles students on my course have been looking at all relate to last weeks assignment and not this weeks.
So that introduces a time element. A student may be interested in what students looked at the last time the assignment was set, which may have been a year ago (for the Open University where some modules run twice a year and some run yearly it might even be a different time period from course to course). So that implies that you might want to introduce a time element into your recommendation algorithm. This would need to check the current date and relate it to the course start date, then use that data and relate it to the last time that the course was run. We discussed that you would need to factor in a window either side to cope with the spread of time that students might be working on an assignment. At the moment for us it’s a moot point as our data only goes back to the end of 2010 so we can’t make those sorts of recommendations anyway. But it’s certainly something that needs to be considered.
(the blog post title owes a lot to Alan Sillitoe’s story and film of the same name ‘the Loneliness of the long-distance runner’)