Explaining recommendations January 8, 2008Posted by Andre Vellino in Collaborative filtering, Social networks.
I heartily agree with Paul Lamere’s comments about making recommendations transparent using explanations. Furthermore, I am convinced that explanations will also provide users with a feedback mechanism to control the recommender’s long-term behaviour. (For a nice survey of explanations in recommender systems, see this paper by Nava Tintarev.)
While Paul’s system of recommendation via social tagging (“tagomendations”) seems to work well for recommending music, I think that for recommending scholarly scientific articles, content analysis techniques (combined with collaborative filtering) are more appropriate. In part this is because the social tagging of, and indeed, the rating of “entertainment” is a form of self-expression. When you rate “Fargo” with 5 stars you are saying something which you want other people to know.
I can also see doing this with books (novels / biographies etc), but not with scholarly articles. For those items, tagging and rating are, at best, a behaviour that users learn will help the automated recommender system do a better job. That’s why I think implicit ratings are more likely to be of value in a scholarly digital library.