  GRank is a recent graph-based recommendation approach the uses a novel
heterogeneous information network to model users' priorities and analyze it to
directly infer a recommendation list. Unfortunately, GRank neglects the
semantics behind different types of paths in the network and during the
process, it may use unreliable paths that are inconsistent with the general
idea of similarity in neighborhood collaborative ranking. That negligence
undermines the reliability of the recommendation list generated by GRank. This
paper seeks to present a novel framework for reliable graph-based collaborative
ranking, called ReGRank, that ranks items based on reliable recommendation
paths that are in harmony with the semantics behind different approaches in
neighborhood collaborative ranking. To our knowledge, ReGRank is the first
unified framework for neighborhood collaborative ranking that in addition to
traditional user-based collaborative ranking, can also be adapted for
preference-based and representative-based collaborative ranking as well.
Experimental results show that ReGRank significantly improves the state-of-the
art neighborhood and graph-based collaborative ranking algorithms.
