Abstract

This thesis will investigate methods to increase the utility of news article recommendation services. Access to dierent news providers allows us to consider cross-domain user preferences. We deal with recommender systems with continuously changing item collections. We will be able to observe user feedback from a real-world recommendation system operating on dierent domains. We will evaluate how results from existing data sets correspond to actual user reactions.

@inproceedings{umap2013:kille,
author = {Kille, Benjamin},
title = {Evaluation of Cross-Domain News Article Recommendations},
booktitle = {Proceedings of the 21st Conference on User Modeling, Adaptation and Personalization},
year = {2013},
isbn = {978-3-642-38843-9},
pages = {363-366},
location = {Rome, Italy},
doi = {10.1007/978-3-642-38844-6_40},
publisher = {Springer Berlin Heidelberg},
address = {...},
}