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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.

Time-sensitive recommendations
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’)

A good thing about projects is the way they lead you to question assumptions about stuff.  And RISE  is doing that in a number of ways.  Not least my assumptions about what recommendations are.  In our terms recommendation services provide information on things that are likely to be of interest to the user but the term ‘recommendations’ is already used elsewhere in libraries but with a subtly different meaning.  We use the term ‘recommendation’ where we might talk about recommended reading, or articles recommended by your course tutor.   And you start to get the sense that there is some form of value hierachy at work here. 

When you start to look at making recommendations based on behaviour (I suppose you could call them ‘derived recommendations’ because they exist as a by-product of search behaviour) then you start to realise that there  may be a difference between recommended reading, meaning something that the course suggests you read; and derived recommendations in terms of which might be of more value to the user (or maybe which has a higher ‘perceived’ value to users).  Now that implies to me that there is some form of hierachy of value in recommendations.  Thinking about it, then it seems to me that the value hierarchy might go something like this:

  1. Recommended readings – listed in your course materials – not things you have to read for your course, but things you should read;
  2. Recommended by the librarian – things the librarian says will be useful;
  3. Recommended by your peers – things that they’ve read and found useful – and that might be peers on your course, or on your social network;
  4. Recommended by other means – so books that seem to be similar (at the same class number on the shelf) for example.

So where do ‘derived recommendations’ fit into that hierarchy model?  Well with RISE we’re looking at several different types.  Recommendations based on what ‘people on your course are looking at’ (Type A), those based on other things that people looked at in the same search sessions (Type B) and recommendations based on having a similar subject to an article you’ve looked at (Type C).   So Type A seems to map quite closely to 3, Type B may also map to 3 but possibly slightly lower, and Type C would seem to equate to 4.

What will be interesting in the testing will be to see how these map out in reality.  What’s interesting to me is that when asked at one of our search focus groups the response to the value of recommendations was that yes they would be useful.  Recommendations from people on your course were useful, but what they would really find useful was knowing what resources were being used by people who got high marks.   That’s a really interesting comment and pretty challenging to be able to tackle that one in a sensible way.  But it also implies that recommendations from people who previously studied a course are particularly valuable which brings into play a timescale issue around recommendations that I need to think about some more.

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