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Neighbor-based collaborative ranking (NCR) techniques follow threeconsecutive steps to recommend items to each target user: first they calculatethe similarities among users, then they estimate concordance of pairwisepreferences to the target user based on the calculated similarities. Finally,they use estimated pairwise preferences to infer the total ranking of items forthe target user. This general approach faces some problems as the rank data isusually sparse as users usually have compared only a few pairs of items andconsequently, the similarities among users is calculated based on limitedinformation and is not accurate enough for inferring true values of preferenceconcordance and can lead to an invalid ranking of items. This article presentsa novel framework, called IteRank, that models the data as a bipartite networkcontaining users and pairwise preferences. It then simultaneously refinesusers' similarities and preferences' concordances using a random walk method onthis graph structure. It uses the information in this first step in anothernetwork structure for simultaneously adjusting the concordances of preferencesand rankings of items. Using this approach, IteRank can overcome some existingproblems caused by the sparsity of the data. Experimental results show thatIteRank improves the performance of recommendation compared to the state of theart NCR techniques that use the traditional NCR framework for recommendation.

IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking