IteRank: An iterative network-oriented approach to neighbor-based collaborative ranking
This work addresses data sparsity issues in recommendation systems for users, but it appears incremental as it builds on existing neighbor-based collaborative ranking methods.
The paper tackles the problem of inaccurate neighbor-based collaborative ranking due to sparse user preference data by proposing IteRank, a novel iterative network-oriented framework that simultaneously refines user similarities and preference concordances, resulting in improved recommendation performance compared to state-of-the-art techniques.
Neighbor-based collaborative ranking (NCR) techniques follow three consecutive steps to recommend items to each target user: first they calculate the similarities among users, then they estimate concordance of pairwise preferences to the target user based on the calculated similarities. Finally, they use estimated pairwise preferences to infer the total ranking of items for the target user. This general approach faces some problems as the rank data is usually sparse as users usually have compared only a few pairs of items and consequently, the similarities among users is calculated based on limited information and is not accurate enough for inferring true values of preference concordance and can lead to an invalid ranking of items. This article presents a novel framework, called IteRank, that models the data as a bipartite network containing users and pairwise preferences. It then simultaneously refines users' similarities and preferences' concordances using a random walk method on this graph structure. It uses the information in this first step in another network structure for simultaneously adjusting the concordances of preferences and rankings of items. Using this approach, IteRank can overcome some existing problems caused by the sparsity of the data. Experimental results show that IteRank improves the performance of recommendation compared to the state of the art NCR techniques that use the traditional NCR framework for recommendation.