Item-based collaborative ranking

This paper presents a novel recommendation approach, PreNIt, that exploits preference networks for item-based collaborative ranking. PreNIt models the users' pairwise preferences as two novel bipartite networks with labeled edges. These labeled edges enable us to model the choice context in which items are preferred/not preferred by the user. Once the networks are constructed, PreNIt finds the transitive similarities of items using a new personalized ranking algorithm in graphs with labeled edges. Experimental results shows the significant outperformance of PreNIt over the state-of-the-art algorithms.