- Relationships between articles e.g. via citations, by being in the same publication
- Relationships between users e.g. linked by clinical interests, by geographic location
- Relationships between articles and users e.g. looking at the same articles
Take a really simple example (click on image to enlarge it)
This is an imaginary search undertaken by 5 users and a line signifies which papers they looked at. We can deduce some things:
- Papers six and seven weren't liked.
- Paper two looks the most popular
- Users 2 and 3 appear similar/close (both looking at two of the same papers)
In this image the rounder reddish boxes signify doctors while the green boxes signify nurses. Do these inferences seem reasonable?
- Paper five is really suited to nurses while papers one (to a point), two and three are more 'doctor' focused
- Paper four has mixed interest.
What do people think? I've really simplified things to make a point and I doubt the data will ever be as clear cut.


2 comments:
Good idea. Definitely worth pursuing, to address the question of if looking at an article is a good measure of a persons interest inan article. One problem though is that once you start flagging articles as 'likely to be of interest to X' you are affecting the underlying data that has led you to flag the article as being of interest to X.
So how would this look to me as the end-user? My instinct is that I wouldn't like how my search results look to change but I wouldn't mind having another frame saying... 'rec'd by others in your network'.
When I do searches I rarely have problems working out what is relevant to me but I guess others may be different...
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