SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking

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.

• Publication year

  2016

• Venue

  arXiv.org

• Publication date

  2016-01-21

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.physa.2016.04.025arXiv 1601.05575
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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