Hey There, Quick questions about the expected behavior when using a customer item similarity model that extends AbstractItemSimilarity within a KnnItemBasedRecommender. Basically I have some logic in doItemSimilarity() that returns 1.0 in certain cases.
When then looking at actual recommendations for a user, I notice that some of the items who should have perfect similarity based on the above logic have wildly varying recommendations. I'm not sure that this is unexpected behavior, but if it is not I'd like to understand why two items with perfect similarity would have different estimated preferences for the same user. The user has not actually rated any of the items in question. Thanks in advance. Nick
