  Collaborative ranking is an emerging field of recommender systems that
utilizes users' preference data rather than rating values. Unfortunately,
neighbor-based collaborative ranking has gained little attention despite its
more flexibility and justifiability. This paper proposes a novel framework,
called SibRank that seeks to improve the state of the art neighbor-based
collaborative ranking methods. SibRank represents users' preferences as a
signed bipartite network, and finds similar users, through a novel personalized
ranking algorithm in signed networks.
