Several sources are reporting on a new metric that computer scientists are going after with respect to recommender systems — recommendation diversity
. "In a paper that will be released by PNAS, a group of scientists are pushing the limits of recommendation systems, creating new algorithms that will make more tangential recommendations to users, which can help expand their interests, which will increase the longevity and utility of the recommendation system itself. Accuracy has long been the most prized measurement in recommending content, like movies, links, or music. However, computer scientists note that this type of system can narrow the field of interest for each user the more it is used. Improved accuracy can result in a strong filtering based on a user's interests, until the system can only recommend a small subset of all the content it has to offer."