I did my grad work in music information retrieval. And one of my committee members, Don Byrd, often had an interesting point as well. He used to say many years ago that he did not want a music recommender that gave him the most similar songs or artists, based on his likes and listening habits. He wanted a recommender that introduced music as different as possible to everything already in his collection. An anti-recommender or “show me something I wouldn’t have found any other way” recommender. I always liked that idea. I suppose its rather similar to “overcome my bias” personalized search system (or would that be anti-personalized?)

I think it has to be a bit of both worlds.

I’ve been thinking recently about the possibility of a recommender deliberately programmed to ignore your biases. For example if you are always reading about politics, perhaps it would give you something about science. I guess the same idea could be applied to search.

What if you mixed up the kinds of results you gave, eg. Sometimes boost sites like wikipedia, other times deliver more academic content? Ie. the system would help you over come biases you didn’t know you had?

You might want to give the user choice on how this functionality works, but it would be an interesting feature.

James