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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