Visualization of Collaborative Data

TL;DR

This paper introduces a Bayesian network-based method for visualizing collaborative data by embedding users and items in a Euclidean space, aiming to reflect their rating relationships effectively.

Contribution

It proposes a novel visualization approach using Bayesian networks and MCMC/EM techniques, with a new metric for assessing visualization quality.

Findings

Our method produces meaningful embeddings that reflect rating patterns.

Compared to existing techniques, our approach offers improved visualization quality.

Experimental results on real datasets validate the effectiveness of our method.

Abstract

Collaborative data consist of ratings relating two distinct sets of objects: users and items. Much of the work with such data focuses on filtering: predicting unknown ratings for pairs of users and items. In this paper we focus on the problem of visualizing the information. Given all of the ratings, our task is to embed all of the users and items as points in the same Euclidean space. We would like to place users near items that they have rated (or would rate) high, and far away from those they would give a low rating. We pose this problem as a real-valued non-linear Bayesian network and employ Markov chain Monte Carlo and expectation maximization to find an embedding. We present a metric by which to judge the quality of a visualization and compare our results to local linear embedding and Eigentaste on three real-world datasets.