- Learning from user interactions for recommending content in social media
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
- We study the problem of recommending hyperlinks to users in social media. We start with a candidate set of links posted by a user's social circle (e.g., friends, followers) and rank these links using a combination of (i) a user interaction model, and (ii) the similarity of a user profile and a candidate link. Experiments on two datasets demonstrate that our method is robust and, on average, outperforms, a strong chronological baseline.
- go to publisher's site
- Proceedings title: Advances in information retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands,
April 13-16, 2014: proceedings
Place of publication: Cham
Editors: M. de Rijke, T. Kenter, A.P. de Vries, C.X. Zhai, F. de Jong, K. Radinsky, K. Hofmann
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.