Magazine recommendations based on social media trends

Steffen Karlsson

AbstractIssuu 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 documentís 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 Issuuís 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.
TypeBachelor of Engineering thesis [Industrial collaboration]
Year2014
PublisherTechnical University of Denmark, Department of Applied Mathematics and Computer Science
AddressMatematiktorvet, Building 303B, DK-2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk
SeriesDTU Compute B.Eng.-2014
NoteDTU supervisor: Ole Winther, olwi@dtu.dk, DTU Compute.
Electronic version(s)[pdf]
Publication linkhttp://www.compute.dtu.dk/English.aspx
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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