  Negative user preference is an important context that is not sufficiently
utilized by many existing recommender systems. This context is especially
useful in scenarios where the cost of negative items is high for the users. In
this work, we describe a new recommender algorithm that explicitly models
negative user preferences in order to recommend more positive items at the top
of recommendation-lists. We build upon existing machine-learning model to
incorporate the contextual information provided by negative user preference.
With experimental evaluations on two openly available datasets, we show that
our method is able to improve recommendation quality: by improving accuracy and
at the same time reducing the number of negative items at the top of
recommendation-lists. Our work demonstrates the value of the contextual
information provided by negative feedback, and can also be extended to signed
social networks and link prediction in other networks.
