  Data sparsity, that is a common problem in neighbor-based collaborative
filtering domain, usually complicates the process of item recommendation. This
problem is more serious in collaborative ranking domain, in which calculating
the users similarities and recommending items are based on ranking data. Some
graph-based approaches have been proposed to address the data sparsity problem,
but they suffer from two flaws. First, they fail to correctly model the users
priorities, and second, they cannot be used when the only available data is a
set of ranking instead of rating values. In this paper, we propose a novel
graph-based approach, called GRank, that is designed for collaborative ranking
domain. GRank can correctly model users priorities in a new tripartite graph
structure, and analyze it to directly infer a recommendation list. The
experimental results show a significant improvement in recommendation quality
compared to the state of the art graph-based recommendation algorithms and
other collaborative ranking techniques.
