I am studying recommender systems algorithms (such as content-based,
or item-to-item recommender systems). What types of recommender
systems exist? How they are classified? Is there somewhere a
comprehensive list of them with an explanation of the different
algorithms?
Thanks

The dominant form of recommender systems are based on collaborative
filtering.  Here are two links to discussions of collaborative
filtering:
1) http://pespmc1.vub.ac.be/COLLFILT.html
2) http://en.wikipedia.org/wiki/Collaborative_filtering

Collaborative filtering is an algorithmic technique for predicting
what a person may want, based on what they have chosen in the past and
based on patterns observed in other peoples' perferences.  To be more
concrete: Suppose Joe liked the movie Casablanca and When Harry Met
Sally.  So, he rated these movies highly.  He didn't like the movies
Predator and Terminator.  So, he raded these movies as poor.  Now, he
wants to find another movie to watch.  He asks the recommender system.
 The recommender system keeps a big array of people and their movie
ratings.  The system finds which people were most similar to Joe in
their ratings (i.e., liked the same movies he liked and disliked the
same movies he disliked).  The idea is that other movies that these
people liked would also be movies that Joe would like.  So, given this
group of people, the system recommends the movie that most of them
liked.