Visualization of Collaborative Data
This addresses the visualization challenge in collaborative filtering for researchers and practitioners, but it is incremental as it builds on existing embedding methods.
The paper tackles the problem of visualizing collaborative data by embedding users and items in a Euclidean space based on ratings, using a Bayesian network with MCMC and EM. It introduces a quality metric and compares results to local linear embedding and Eigentaste on three real-world datasets.
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.