Algorithms for item categorization based on ordinal ranking data

TL;DR

This paper introduces a community detection-based method for item categorization from ordinal ranking data, converting rankings into a graph and applying a modified label propagation algorithm, validated on synthetic and real datasets.

Contribution

It demonstrates how existing community detection algorithms can be adapted for item categorization from ranking data, linking ranking models to stochastic block models.

Findings

Effective categorization on synthetic data

Successful application to Movie Lens dataset

Algorithm outperforms some existing community detection methods

Abstract

We present a new method for identifying the latent categorization of items based on their rankings. Complimenting a recent work that uses a Dirichlet prior on preference vectors and variational inference, we show that this problem can be effectively dealt with using existing community detection algorithms, with the communities corresponding to item categories. In particular we convert the bipartite ranking data to a unipartite graph of item affinities, and apply community detection algorithms. In this context we modify an existing algorithm - namely the label propagation algorithm to a variant that uses the distance between the nodes for weighting the label propagation - to identify the categories. We propose and analyze a synthetic ordinal ranking model and show its relation to the recently much studied stochastic block model. We test our algorithms on synthetic data and compare performance with several popular community detection algorithms. We also test the method on real data sets of movie categorization from the Movie Lens database. In all of the cases our algorithm is able to identify the categories for a suitable choice of tuning parameter.