Netflix and Amazon.com ease your selection process by recommending movies or books that you may like based on your history. Recommender systems been successful in e-commerce and other domains in which information overload exists. What about educational scenarios where, arguably, a wealth of information is potentially relevant?
From Olga C. Santos, aDeNu Research Group, UNED, Spain:

Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications (i.e. helping users to select the most appropriate item from a large information pool), there are some particularities that make it impossible to directly apply existing solutions from those systems. For instance, recommendations in the educational domain should not be guided only by the learners’ preferences but also by educational criteria. However, most ERS approaches have focused on applying traditional recommendation algorithms in order to find relevant resources for learners in learning scenarios. While this approach is pointing at interesting open issues, there are complementary views in this field to address the current challenges, which may clarify grounds to successfully deploy ERS.

Olga is working on a book on this topic. She has proposed a recommendation model for e-learning, but most of the elements in it are generalizable to other domains (i.e. healthcare). She also proposed a user-centered design methodology to elicit relevant recommendations from experts:

The reason for this (and not to start applying the algorithms as it is typically done in e-commerce recommenders) is that first, I need to find out what are relevant recommendations for e-learning. Afterward, algorithms can be applied to reproduce the elicited recommendations in an automatic way. I guess the same will happen in the healthcare domain, you should first need to involve experts to identify the recommendation needs.

Fascinating topic. Who doesn’t suffer from information overload – and crave intelligent assistance?