  Implicit feedback is the simplest form of user feedback that can be used for
item recommendation. It is easy to collect and domain independent. However,
there is a lack of negative examples. Existing works circumvent this problem by
making various assumptions regarding the unconsumed items, which fail to hold
when the user did not consume an item because she was unaware of it. In this
paper, we propose as a novel method for addressing the lack of negative
examples in implicit feedback. The motivation is that if there is a large group
of users who share the same taste and none of them consumed an item, then it is
highly likely that the item is irrelevant to this taste. We use Hierarchical
Latent Tree Analysis(HLTA) to identify taste-based user groups and make
recommendations for a user based on her memberships in the groups.
