SibRank: Signed Bipartite Network Analysis for Neighbor-based Collaborative Ranking
Bita Shams, Saman Haratizadeh

TL;DR
SibRank introduces a novel framework for neighbor-based collaborative ranking by representing user preferences as a signed bipartite network and employing a personalized ranking algorithm to identify similar users.
Contribution
It proposes a new signed bipartite network model and a personalized ranking algorithm to enhance neighbor-based collaborative ranking methods.
Findings
Improves accuracy of neighbor-based collaborative ranking
Effectively models user preferences with signed networks
Demonstrates superior performance over existing methods
Abstract
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.
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