I would like to know how exactly mahout user based and item based recommendation differ from each other.
It defines that
User-based: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of users.
Item-based: Calculate similarity between items and make recommendations. Items usually don't change much, so this often can be computed off line.
But though there are two kind of recommendation available, what I understand is that both these will take some data model ( say 1,2 or 1,2,.5 as item1,item2,value or user1,user2,value where value is not mandatory) and will perform all calculation as the similarity measure and recommender build-in function we chose and we can run both user/item based recommendation on the same data ( is this a correct assumption ?? ).
So I would like to know how exactly and in which all aspects these two type of algorithm differ.