@incollection{
    klapuri2013ida,
    editor = {Tucker, Allan and H{\"o}ppner, Frank and Siebes, Arno and Swift, Stephen},
    author = "Klapuri, Jussa and Nieminen, Ilari T. and Raiko, Tapani and Lagus, Krista",
    isbn = "978-3-642-41397-1",
    series = "Lecture Notes in Computer Science",
    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.",
    month = "October",
    il = "no",
    year = "2013",
    title = "Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset",
    eventlocation = "London, UK",
    booktitle = "Advances in Intelligent Data Analysis XII",
    juforank = "-",
    unitcode = "T306",
    impactfactor = "A4",
    eventtime = "October 17--19",
    responsibleauthor = "Klapuri, Jussa",
    volume = "8207",
    address = "London, UK",
    kay = "NA",
    pages = "249--260",
    publisher = "Springer Berlin / Heidelberg",
    language = "eng",
    url = "http://link.springer.com/chapter/10.1007/978-3-642-41398-8_22",
    corerank = "A",
    flags = "COIN AIRC COG public"
}