SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

• SibRank is a new recommendation algorithm based on similarity among users’ ranking.
• SibRank models users’ ranking as a novel signed bipartite network structure.
• SibRank exploits signed multiplicative rank propagation for similarity calculation.
• SibRank is able to calculate similarity between users without any common ranking.
• SibRank improves NDCG@10 up to 5% compared to other collaborative ranking methods.

Collaborative ranking is an emerging field of recommender systems that utilizes users’ preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users’ preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.