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

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.