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R-LAIR: Riverside Lab for Artificial Intelligence Research

Visualization of Collaborative Data (2006)

by Guobiao Mei and Christian R. Shelton

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 low ratings. 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 Eigentaste, locally linear embedding and co-occurrence data embedding on three real-world datasets.

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Guobiao Mei and Christian R. Shelton (2006). "Visualization of Collaborative Data." Proceedings of the Twenty-Second International Conference on Uncertainty in Artificial Intelligence (pp. 341-348). pdf ps    

Bibtex citation

@inproceedings{MeiShe06,
   author = "Guobiao Mei and Christian R. Shelton",
   title = "Visualization of Collaborative Data",
   booktitle = "Proceedings of the Twenty-Second International Conference on Uncertainty in Artificial Intelligence",
   booktitleabbr = "{UAI}-2006",
   year = 2006,
   pages = "341--348",
}

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