Any time you are making an estimate, you have a loss function that expresses how much you like or dislike different estimates. In this memory based approach, you have several ratings that you would some how like to combine to get an estimate of the best predicted rating.
It is common to combine these ratings using a weighted average. Some approaches, however, come up with negative weights. In order to understand how to deal with negatively weighted examples, you have to go back to the underlying mathematics that is beneath the weighted average. That mathematics is expressed in terms of a quadratic loss function. This gives you three possibilities, one where positive weights dominate, one where negative weights dominate and a third where they balance out. The examples I gave were in terms of the result of querying the similar users for movies with ratings. On Tue, Feb 23, 2010 at 12:54 PM, Tamas Jambor <[email protected]>wrote: > not sure if I understand your examples. I thought this is not really a 'the > loss function' since, these are memory based approaches, so there is > no training in the classical machine learning sense. >
