Reference:

Jussa Klapuri, Ilari T. Nieminen, Tapani Raiko, and Krista Lagus. Variational bayesian pca versus k-nn on a very sparse reddit voting dataset. In Allan Tucker, Frank Höppner, Arno Siebes, and Stephen Swift, editors, Advances in Intelligent Data Analysis XII, volume 8207 of Lecture Notes in Computer Science, pages 249–260. Springer Berlin / Heidelberg, October 2013.

Abstract:

We present vote estimation results on the largely unexplored Reddit voting dataset that contains 23M votes from 43k users on 3.4M links. This problem is approached using Variational Bayesian Principal Component Analysis (VBPCA) and a novel algorithm for k-Nearest Neighbors (k-NN) optimized for high dimensional sparse datasets without using any approximations. We also explore the scalability of the algorithms for extremely sparse problems with up to 99.99% missing values. Our experiments show that k-NN works well for the standard Reddit vote prediction. The performance of VBPCA with the full preprocessed Reddit data was not as good as k-NN’s, but it was more resilient to further sparsification of the problem.

Suggested BibTeX entry:

@incollection{klapuri2013ida,
    author = {Jussa Klapuri and Ilari T. Nieminen and Tapani Raiko and Krista Lagus},
    booktitle = {Advances in Intelligent Data Analysis XII},
    editor = {Allan Tucker and Frank H{\"o}ppner and Arno Siebes and Stephen Swift},
    month = {October},
    pages = {249--260},
    publisher = {Springer Berlin / Heidelberg},
    series = {Lecture Notes in Computer Science},
    title = {Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset},
    volume = {8207},
    year = {2013},
}