@MISC\{IMM2014-06729,
    author       = "S. Karlsson",
    title        = "Magazine recommendations based on social media trends",
    year         = "2014",
    publisher    = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Matematiktorvet, Building 303B, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    note         = "{DTU} supervisor: Ole Winther, olwi@dtu.dk, {DTU} Compute.",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "Issuu uses a recommendation engine, for predicting what a certain reader will enjoy. It is based on collaborative filtering, such as reading history of other similar users and content-based filtering reflected as the document’s topics etc. So far all of those parameters, are completely isolated from any external (non-Issuu) sources causing the Matthew Effect. This project, done in collaboration with Issuu, is the first attempt to solve the problem, by investigating how to extract trends from social media and incorporate them to improve Issuu’s magazine recommendations.
Popular social media networks have been investigated and evaluated resulting in choosing Twitter as the data source. A framework for spotting trends in the data has been implemented. To map trends to Issuu two approaches have been used - Latent Dirichlet Allocation model and Apache Solr search engine."
}