I’ve offered the idea that good recommendations lie on a scale between Obvious and Interesting. Taken to their extreme, the full line could actually run from Boring through to Random, with Obvious and Interesting somewhere in between. ‘Boring’ recommendations could be said to exist where the Advisor’s Preference Set is entirely made up of the Common Set (i.e. the Advisee knows at least as much as the Advisor). ‘Random’ recommendations could be said to exist where there is no Common Set at all (i.e. A disjoint B )

I’ve also attempted to explain my thoughts on the kinds of relationships that could exist between an Advisor and an Advisee, and ways in which they could be elaborated. I think I’ve done that, albeit in a not very scientific way. So there’s a lot of room for refinement, and there are some gaps to be filled (specifically around calculating variation within a set of preferences and analysing effects of different weightings of those preferences) and some of my assumptions are a little more tenuous than maybe they should be, but I think this could provide the basis for some interesting results.

I’m also keen to explore the possibility that subjective relationships (rather than behavioural relationships) between Things would produce better/more interesting recommendations and routes for discovery. For my MSc project, I’m intending to focus on the Music Domain, analysing music blogs to deduce relationships between artists to augment recommendations from existing services such as last.fm.