Online Pairwise Ranking Based on Graph Edge–Connectivity

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 424)

Abstract

We propose a novel ranking algorithm that takes into account specific properties of the graph that represents the items and the user votes in pairwise comparison scenarios. The algorithm models the scoring relationships between instances as local edges among vertices in a corresponding graph and use such properties to find scores for each instance. We have compared the performance of the algorithm with other widely known information retrieval techniques tasked with ranking a set of movies. As a baseline implementation, we have used the topological ordering of the acyclic subgraph with maximum weight, by solving an approximated version of the maximum acyclic subgraph problem. The results show accurate ranking lists for the movie dataset.

Keywords

Online ranking MASP Vertex–connectivity Machine learning 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Universidad Santo TomásBogotáColombia
  2. 2.Universidad de Los AndesBogotáColombia

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